How Academic Careers Actually Work

(and what to do about it)

Boris Gorelik, PhD

Loud Camel

2026-05

© 2026 Boris Gorelik. Licensed under CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/).

How Academic Careers Actually Work

How Academic Careers Actually Work

(and what to do about it)

"Nobody cares about your work. So you'd better."

A field guide to academic visibility, grounded in what the science actually says.

Boris Gorelik, PhD

Edition 2026-05

How to read this handbook

You spent six years learning to do science. Nobody spent six minutes teaching you how to make sure anyone notices. This handbook is the missing half.

There are three parts and you can read them in any order, but reading them in this order is the point.

Part I: How academic success actually works. Five short chapters on what the peer-reviewed literature says about citations, recognition, and the gap between the quality of your work and the recognition it gets. Read this part if you’ve ever submitted a paper and watched it disappear, and assumed it was something you did wrong.

Part II: What you can actually do this week. Nine tactics. Each one is short, each one is anchored to a result from Part I, and each one ends with a checklist. If you only have one afternoon, skip to Part II and come back for the why.

Part III: Templates and ChatGPT prompts. Copy-paste material. Outreach emails, a prompt that turns your abstract into a lay summary a grant reviewer will read, a prompt for auditing whether ChatGPT and Perplexity know your work exists. Bring your laptop.

My goal here is not to make you feel good. It is to tell you what the peer-reviewed literature actually says about how academic careers work, including the parts that are uncomfortable, and to derive a small number of actions you can do this week. Where the literature says something clearly, this handbook says it clearly. Where it doesn’t, this handbook says that too. Career advice loaded with “might”, “may”, and “one could argue” is unreadable. So this isn’t that.

Part I rests on the framework of five “laws of success” laid out by network scientist Albert-László Barabási in his 2018 book The Formula. If you want the long version of the science, that book is worth your time. This handbook does three things The Formula does not. First, it cites the peer-reviewed primary papers directly for every claim, so you don’t take a popular synthesis on faith. Second, it updates the picture for the LLM era, where the citation feedback loops Barabási wrote about have a new amplifier (Chapter 3 sidebar). Third, and this is the actual product, it translates the science into Parts II and III: an operational playbook of nine tactics and nine copy-paste prompts. The science is the setup. The playbook is what gets you citations.

This handbook is published by Loud Camel — a system that runs all nine tactics for you on a recurring schedule. https://loudcamel.com

Why this handbook

The thing nobody tells you in graduate school: from your first submitted paper onward, half of the job is making sure the right people know your work exists. Not once you have your own research program. Now. Every postdoc offer, every faculty short-list, every tenure vote, every grant ranking is decided in part by people whose prior familiarity with your work, however slight, tilts the outcome. You were not trained for that half. Almost nobody is.

You know what’s depressing? Watching a paper you spent two years on get four citations in three years, while a less rigorous paper in your field (same journal, same topic) gets a hundred and twenty. You read it. It’s fine. It’s not that much better than yours. The author just happens to be in a famous department, happens to give a talk every six weeks, happens to have a social-media following. Their paper compounds. Yours sits.

You might think: I’ll do the job and let the results speak for themselves. Or maybe my supervisor will promote me. My department will rally behind its own. Sometimes that happens. Often it doesn’t. Your supervisor is busy promoting their own next grant, and the department is busy with the people whose visibility is already established. Cultures that have been around long enough have all noticed this. A Soviet-era proverb, from Ilf and Petrov’s The Twelve Chairs, reads in literal English: the rescue of the drowning is the responsibility of the drowning themselves. An older Jewish version, attributed to Hillel two thousand years earlier and recorded in the Mishnah: if I am not for myself, who is for me? Both are talking about exactly the same observation. Nobody is going to do this for you. Especially not in academia.

This is not a moral problem, and it is not a bug. It is an intrinsic feature of any human system where individual quality is hard to measure objectively. It has been studied for decades. The results are not flattering to the romantic version of how science works (the version where the best work rises and is recognized), and they are also not new. Robert Merton wrote about the Matthew effect in Science in 1968.1 Albert-László Barabási spent the 2010s turning the dynamics into equations.² The verdict, in one sentence: in any field where performance is hard to measure, success is driven by what people already think about you, not by the work itself.

Academia is exactly such a field. The work is hard to measure. Two papers can be equally rigorous and one gets cited fifty times more, for reasons that have nothing to do with rigor. The fifty-times-more paper is not gaming the system. It is being noticed by the system. There is a difference.

Case study: the budget-pitch project

Years before Loud Camel, I worked on a project at RSA, the security division of DELL Inc. A second team was running a parallel project. Both were good. The company had budget to continue exactly one.

In the last three weeks before the decision, my team made an explicit choice: we stopped improving our models and put the time into an interactive presentation. The competing team kept improving theirs.

We won the budget.

Honest reaction: part of me felt that wasn’t fair. Another part noticed, calmly, that we had not done anything dishonest. Our models were good. Their models were good. The decision-maker could see and follow our work and could not see and follow theirs. That is what the budget paid for.

That experience is most of what convinced me the mechanism in this section is not an academic pathology. It is how human attention markets work, in companies, on grant panels, at conferences. The corporate-academic border does not change the rules. The hard part stays the same: do good work. The unequally-distributed part is making sure it can be seen and followed.

Most researchers respond to this in one of three ways:

Denial. “If I just do better work, it will be recognized.” This is the romantic version. It is wrong. Or rather, it is half-right: you do need to do good work, but doing good work is necessary and not sufficient. The literature is unambiguous on this.

Despair. “The system is rigged, so why bother.” This is the cynical version. It is also wrong. The same studies that show the system is unfair also show that the system is responsive, and that small, repeated visibility actions compound over years in ways that are not subtle.

Burnout. “I’ll just post more on X / YouTube / Medium.” This is the personal-branding version. It is the worst of the three for most researchers because it is exhausting and off-brand. You did not get a PhD to become a content creator. A small handful of researchers do make platform-native self-promotion work, and work well; the sidebar opposite profiles one of them. It is possible. It is just not the route that fits most academic careers, and pretending it does is how researchers burn out trying to be someone they are not.

Profile: Stephen Wolfram, doing it right

[picture]

Stephen Wolfram. Credit: Stephen Wolfram's PR team / Stephen Faust, via Wikipedia, CC-BY-SA.

Twitch is mostly associated with gamers and streamers. Stephen Wolfram, the physicist who founded Wolfram Research and built Mathematica and the Wolfram Language, uses it to broadcast hours-long live science streams: debugging the design of new language features, working through computational problems on camera, taking audience questions in real time. The streams are not packaged content. They are the actual thinking, in public. I consider him a role model for what a scientist-entrepreneur can be on a public platform, and the streams are the proof of concept.

What makes it work for him and not for most of us: he isn’t translating his work for the platform; the platform is broadcasting his work.

The lesson generalizes: if your equivalent of “the actual thinking” can be public, do it. If it can’t, posting more on X will not save you.

The frame this handbook proposes is a fourth one: strategic, low-frequency, low-effort, science-backed visibility. The thing you should care about is that the work is good (you already do) and that the right people encounter it at the right time (you don’t, and nobody trained you to). The whole handbook is one long argument for the fourth frame, with the tactics that follow from it.

Nobody cares about your work. You should care the most.

That is not a complaint. It is a permission slip. Stop waiting for the field to notice. It won’t. Not because the field is hostile, but because the field is busy.

1. Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 56–63.

2. Barabási, A.-L. (2018). The Formula: The Universal Laws of Success. Little, Brown and Company. Each of the five “laws” is a popular synthesis of underlying peer-reviewed papers, cited in the chapters below.

Part I: How academic success actually works

Five chapters. Each one is a Barabási “law” anchored to a peer-reviewed primary source. Read them in order. They build.

[picture]

Albert-László Barabási, network scientist. Source: World Economic Forum via Wikipedia, CC-BY-SA.

Barabási is a Romanian-born Hungarian-American physicist and network scientist at Northeastern University, and the connective tissue of Part I. The five “laws” are his synthesis from the 2018 popular book The Formula: The Universal Laws of Success. Each one rests on a peer-reviewed primary paper, which the chapters below cite directly so you don’t have to take a popular synthesis on faith.

The five laws

L1. When performance can be measured (sports), performance drives success. When it can’t (science, art), networks drive recognition.

L2. Performance is bounded; success is not. Citations follow a power law.

L3. Prior success × the inherent quality of your work = future success. The rich get cited.

L4. Teams produce most of the impact; one author gets most of the credit.

L5. Your single biggest paper is statistically independent of where in your career it occurs. Quitting is the only failure mode.

Chapter 1. Performance vs. success (L1)

Academic “performance” is barely measurable. So recognition is mostly social. This is not a moral failing. It is a structural feature of any field where output quality cannot be reduced to a single number.

In 2006, Matthew Salganik, Peter Dodds and Duncan Watts ran one of the cleanest social experiments in the history of social science.¹ They set up an online music store. Users could listen to 48 songs by unknown bands, rate them, and download what they liked. The users were randomly assigned to one of two worlds.

In world A, users saw only the songs and their own opinions.

In world B, users saw the same songs, plus a real-time download counter showing how many other people had downloaded each track. Importantly, world B was split into eight independent sub-worlds, each starting with zero downloads. Any popularity signal had to grow organically inside that sub-world.

Same songs. Same listeners. Same starting conditions. The only difference: in world B, you could see what other people were doing.

What happened next is the whole basis of this handbook. In world A (the no-signal world) the songs the listeners rated highest tended to be downloaded most. Quality predicted success, weakly but predictably. In world B, the social-signal worlds, the link between quality and success became much noisier. Songs that got lucky early, for whatever reason, accumulated downloads, visibly accumulated downloads, and that visible accumulation drove more downloads. Songs that got unlucky early stayed unlucky. The same song could be a hit in one sub-world and a flop in another.

Same songs, different worlds

Inequality: in the worlds where users could see the download counter, the gap between the most-downloaded and least-downloaded songs was much larger than in the worlds where they could not. The same songs were “hits” in some sub-worlds and “flops” in others, driven by which song happened to be downloaded first. Conclusion: in any field where people can see what other people are doing, success runs on perceived popularity at least as much as on quality. Source: Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854–856.

You can already see where this is going. Academia is world B, with the lights turned all the way up. Citations are public. Conference invitations are public. h-indexes are public. Every reader of your paper can see who else has cited it. The signal is loud, and the signal feeds itself.

The Salganik experiment also has a sting in the tail. When the experimenters secretly inverted the download counts in world B (relabeling the actually-best songs as the worst and vice versa), the originally-best songs eventually clawed back some of their popularity, but not all of it. Quality has a floor, but the floor is much lower than your advisor told you it was.

Why this matters for you

Two papers can be equally rigorous and equally interesting and one gets cited fifty times more. That is the expected outcome in a field where performance is hard to measure and the signal of past success is loud. Treating this as a personal failure is a category error. Treating it as a feature of the system that you can deliberately work with is the entire premise of Part II.

The one thing you can implement NOW

Open your LinkedIn profile. Replace the headline (“Postdoctoral Researcher at X University”) with a one-sentence description of what you actually work on, in plain language. Five minutes. From this moment forward, every person who lands on your profile sees a visible signal of what you do, instead of a generic job title that says nothing. Salganik’s experiment was about visible signals; this is the cheapest one you control.

Chapter 2. Citations are unbounded; you are not (L2)

Performance has a ceiling (you can only run so fast). Success doesn’t. Citations follow a power law, which is one of the most common mathematical shapes that emerges in nature and in human systems: a small number of papers collect almost all the citations, the median paper gets almost none. This is the shape of the field whether you like it or not.

Take any sub-discipline. Pull the citation counts for every paper published in the last decade. Sort them. Plot them on a log scale.

You will get a power law.² A very small number of papers will sit at the top with thousands of citations. A long tail of papers (the majority of papers) will sit near the bottom with one citation, two citations, sometimes zero. The median paper in most fields collects about one citation per year, total. The 99th-percentile paper collects thousands.

This is not a problem with science. It is the shape of any system where success feeds back into more success. Movies do this. Books do this. Songs (see Chapter 1) do this. Researchers’ careers do this. So do earthquake magnitudes, the sizes of cities, the distribution of personal wealth, and the frequency with which any word appears in any language. Power laws are not a quirk of academia; they are one of the most common shapes in nature and society, and the underlying mechanism is well-understood. The mathematical name is “preferential attachment”, which Barabási, Albert and Jeong wrote about in 1999 in the context of the world wide web,³ and which turns out to describe citation networks just as well.

The hockey-stick shape

Power law: citation distributions across many disciplines follow a heavy-tailed, approximately power-law shape. A small fraction of papers accumulates the bulk of all citations; the median paper accumulates roughly one citation per year. Conclusion: trying to be “above average” is the wrong frame. The field is shaped like a hockey stick. Above average doesn’t get you noticed; visibly above average does. Sources: Radicchi, F., Fortunato, S., & Castellano, C. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. PNAS, 105(45), 17268–17272. Wang, D., Song, C., & Barabási, A.-L. (2013). Quantifying long-term scientific impact. Science, 342(6154), 127–132.

The Wang–Song–Barabási 2013 paper deserves a second look. They modeled citation history as the combination of three things: the paper’s intrinsic quality, how fast the field forgets old work, and a feedback loop where cited papers get more cited. The third term is the one that matters. It is the engine that turns a small early lead into a huge late lead.

The implication is uncomfortable. Two papers with the same fitness can diverge dramatically over the same 5–10 year window, just because one got a couple of early citations and the other didn’t. The early citations don’t have to be from famous people. They just have to exist. After that, the feedback loop does the rest.

One thing has changed since the Wang–Song–Barabási paper was published: LLMs. ChatGPT, Perplexity and Google AI Overviews are trained on text in which mentions of researchers are themselves power-law distributed. Wikipedia pages, news writeups, blog coverage, podcast appearances all cluster heavily around the already-visible. The model has orders-of-magnitude more text about the already-visible cohort than about everyone else. When you ask an LLM “who are the important people in X?”, the answer is shaped by that prior distribution. The LLM is not flattening the citation hockey stick; it is riding it. The early-citation seeding effect is now mirrored by an early-LLM-mention seeding effect, and the two feed each other.

Why this matters for you

The first ten citations of your paper are doing disproportionate work. They are not just citations; they are the seed of every subsequent citation through the Wang–Song–Barabási feedback term. Every action in Part II that helps you get early citations (the preprint, the plain-language summary, the email to the people whose work you cited, the discussion post) is leveraging this mechanism. You don’t need a lot. You need some. Early.

The one thing you can implement NOW

Open Google Scholar. Find one paper from the last 6 months in your sub-field that should have cited your relevant work but did not. Send the author a one-paragraph email, today: “I saw your recent paper on X; you may want to look at our 2024 paper on the closely related Y.” Ten minutes. That is one seed in the feedback loop. The Wang–Song–Barabási model says you don’t need many; you need a few, early.

Chapter 3. The rich get cited (L3)

Prior success × your work’s inherent quality = future success. Robert Merton called this the Matthew effect in 1968. Six decades of work since has not weakened it.

Merton’s 1968 paper is genuinely funny if you are in the mood for academic deadpan. It opens with the Gospel of Matthew (“For unto every one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken away even that which he hath”) and then proceeds, in the most sober Science-paper voice, to show that this is approximately how scientific recognition works.⁴ Famous scientists get credit for discoveries that less-famous co-discoverers also made. The same paper gets cited more when the author has a big name on it. Multiple independent discoveries default to the bigger name in the textbook.

This sounds like a bug. It isn’t. It is the L2 feedback loop from Chapter 2 acting on individual people instead of individual papers. Once you are visible, the system finds you faster. Reviewers nominate you for journal special issues. Conference organizers invite you to speak. PhD students cite you because you came up in their literature search. Each of those things increases your visibility, which increases the next round.

Petersen and colleagues (2014) put numbers on this for academic careers specifically.⁵ They followed thousands of researchers across multiple fields. Once a researcher’s reputation (defined operationally as cumulative citations) passes a threshold, the citations to their next paper become measurably decoupled from the paper’s individual quality. The system starts citing the person, not the work.

The visibility threshold

A measurable threshold exists: once a researcher’s cumulative-citation count passes a field-specific threshold, the citations to their next paper become measurably decoupled from that paper’s individual quality. The system starts citing the person, not the work. Conclusion: the first ~100 citations of your career are doing the disproportionate work. After that, the Matthew effect runs with you instead of against you. The job is to get over the threshold. Source: Petersen, A. M., Fortunato, S., Pan, R. K., Kaski, K., Penner, O., Rinaldo, A., Riccaboni, M., Stanley, H. E., & Pammolli, F. (2014). Reputation and impact in academic careers. PNAS, 111(43), 15316–15321.

This is the chapter where it is tempting to conclude the system is rigged, the system has always been rigged, why bother. Don’t. The same papers that show the rich get cited also show the threshold is real. Plenty of researchers cross it every year. The ones who do are not, on average, the most brilliant; they are the most visible. The ones who don’t are not, on average, the least brilliant; they are the least visible. This is not a moral judgment. It is the operational definition of the Matthew effect.

LLMs are the new Matthew engine

The Matthew effect now has a new engine, and there is empirical evidence that it is not subtle.

Algaba et al. (2025a) tested GPT-4, GPT-4o and Claude 3.5 on 166 papers from top machine-learning venues (AAAI, NeurIPS, ICML, ICLR).⁶ They asked the LLMs to suggest references for each paper, then compared the LLM output to the paper’s actual reference list. The LLM-suggested references had substantially higher median citation counts than the real references, even after controlling for publication year, venue, title length, and number of authors. A follow-up study of 10,000 papers and roughly 275,000 LLM-generated references shows the same bias toward already-highly-cited work persists across scientific domains.⁷

The mechanism: LLM training data is itself power-law distributed. Famous scientists have Wikipedia pages, news writeups, podcast appearances; everyone else has a Google Scholar entry. When a user asks an LLM “who are the important people in X?”, or when an LLM is used to draft a related-work section, the answer is shaped by that prior distribution. The names on the list get cited. The names not on it stay un-found.

Network scientists have a name for the resulting structure: a hub-and-spoke graph in which a few super-hubs collect the bulk of the attention. LLMs are a new amplifier on this graph, not a leveler of it.

The empirical evidence so far is about citation-pattern bias, not about demonstrated effects on hiring, promotion or grant outcomes; the longitudinal career-level data is years away.⁸ But citation-pattern bias is exactly the mechanism that Wang–Song–Barabási and Petersen et al. predict will drive career trajectories.

The handbook’s tactics respond directly. Tactic 1 (profile hygiene) is the price of admission. Tactic 4 (plain-language summaries) is what the LLM quotes back. Tactic 7 (public conversations) is how you get into the next training set.

Why this matters for you

You need a small number of visible seeds (citations, mentions, conversations) before the feedback loop can pick you up. Every tactic in Part II is in some way about generating those seeds with minimal effort, on a schedule that doesn’t burn you out.

The one thing you can implement NOW

Open your Google Scholar profile. Count the papers listed. Compare against your CV. Most researchers find 1–3 missing or duplicated papers. Fix them today. Every missing paper is a missing citation seed for the rest of your career. Every duplicate is the Matthew engine splitting credit between two half-yous. Ten minutes. This is the cheapest threshold-crossing action available to you.

Chapter 4. The credit asymmetry of team science (L4)

Teams produce most of the impact. One author gets most of the credit. If you are not the first or last author, you are probably under-credited regardless of how much you contributed.

Wuchty, Jones and Uzzi (2007) looked at almost 20 million papers and 2 million patents going back to 1955.9 They asked one question: are teams or individuals producing the high-impact work? The answer is now well-known and was at the time a small bombshell. Across almost every discipline, teams have been displacing solo authors for fifty years, and team-authored papers are cited substantially more often.

Teams produce the impact

~6×: in the sciences, the average team-authored paper is cited about six times more often than the average solo-authored paper from the same field and era. The gap has been growing since the 1950s, in every discipline studied. Conclusion: solo work is no longer the default vehicle for impact, even in fields that romanticize the solo genius. If you are solo, you are working against the trend. Source: Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.

So teams produce the impact. But who gets the credit?

Shen and Barabási (2014) attacked this question with a clever method.¹⁰ They modeled how the scientific community implicitly assigns credit on multi-author papers, then validated the model against cases where credit was unambiguous (Nobel prizes, prestigious awards, prize lectures). The result is uncomfortable: on a typical multi-author paper, the first author receives the lion’s share of the perceived credit; the last author (typically the senior author, in fields that use that convention) receives most of the rest; the middle authors are statistical noise. In fields that use alphabetical authorship (notably economics and parts of mathematics), the asymmetry is different, but a credit asymmetry of some kind still exists. It just attaches to seniority and visibility rather than position.

Credit is winner-takes-most

Credit is highly skewed: on a typical multi-author paper across many disciplines, the first author captures the majority of perceived credit, with most of the remainder going to the senior (last) author. Middle authors are largely invisible to the credit-assignment process unless they take extra visibility actions. Conclusion: in collaborations, fight for first-author position when you did first-author work. If you can’t, fight for the corresponding-author footnote, the public-facing role at the conference, and the version of the figure that has your name on it. Source: Shen, H.-W., & Barabási, A.-L. (2014). Collective credit allocation in science. PNAS, 111(34), 12325–12330.

The combined message of L4 is: you should collaborate (impact lives in teams) and you should be deliberate about how you collaborate (credit doesn’t redistribute itself). Collaborations are not free. Every collaboration you enter as a middle author is a year of work that probably will not be visible to a future evaluator. That is sometimes worth doing. It is rarely worth doing by default.

Why this matters for you

Two practical consequences. First, on the team axis: prefer projects where you are the first or last author, or where you have a clearly attributable contribution (the methods, the dataset, the software package). Second, on the visibility axis: when a multi-author paper of yours comes out, the publication itself does not make your contribution visible; the first author’s promotion does. If you are middle-author on something you actually drove, you need to do your own visibility work on top of the publication. The plain-language summary, the discussion post, the talk: these are how you reclaim the credit the citation network won’t give you.

The one thing you can implement NOW

Pick a paper where you were a middle author on something you actually drove (the method, the dataset, the analysis). Write two sentences naming your specific contribution. Post those two sentences on your personal site, your ORCID, or your LinkedIn, attached to that paper. Five minutes. This is how you reclaim credit the citation network won’t give you: by saying out loud what you did, before the network forgets.

Chapter 5. The Q-factor, or why you should not quit (L5)

Your biggest paper has not happened yet, and that statement is true regardless of where you are in your career. The single biggest predictor of your single biggest hit is that you are still publishing when it arrives.

This is the chapter that should be tattooed on the inside of every postdoc’s eyelids.

Sinatra, Wang, Deville, Song and Barabási (2016) looked at the careers of thousands of scientists across multiple disciplines.¹¹ They asked, for each researcher, where in your career did your single most-cited paper appear? The result, which they called the random impact rule, is one of the most consequential findings in the science-of-science literature, and one of the least known.

The biggest paper of a researcher’s career is statistically uniformly distributed across the productive part of that career. It is not more likely to come from the early-career “creative phase”, as the conventional wisdom holds. It is not more likely to come from the late-career “wisdom phase”, as the alternative conventional wisdom holds. It is just as likely to be your 3rd paper as your 30th. The only requirement is that you produced enough papers for the lottery to have a ticket in it.

The random impact rule

Your biggest hit is statistically independent of when in your career it arrives. What predicts the height of your career peak is your individual Q-factor (a stable, individual “ability” parameter) multiplied by the inherent quality of the paper-shaped lottery ticket you happened to draw. Conclusion: the dominant career mistake is not “I didn’t have my breakthrough early enough”. The dominant career mistake is quitting before the breakthrough arrives. There is no statistical justification for “I’m too old / too late / too far behind”. Source: Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A.-L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239.

There is a second, more humbling, finding in the same paper. The Q-factor, the individual “ability” parameter, is largely set early and stays approximately constant across a career. You don’t get more talented at having impactful ideas as you age; you also don’t get less. What you do is keep buying tickets, and the tickets are independent draws.

If you take L5 seriously, two career-defining behaviors follow.

First: publication volume in your area is not a vanity metric, it is a sample-size argument. Every paper is a draw from a distribution whose top is statistically independent of paper number. More papers = more chances to hit a big one. This is not an argument for spammy LPUs (least publishable units). It is an argument against the perfectionism that produces zero papers a year.

Second: don’t quit. Most career advice frames quitting as a complex, contextual decision. The L5 data says it is much simpler than that. The biggest predictor that you will not have a high-impact paper is that you stop writing them.

This is the most encouraging finding in the literature. It is also, statistically, the most under-acted-on.

The one thing you can implement NOW

Open your calendar. Pick a date in the next 8 weeks. Put “first draft of next paper, complete” on that date. The paper doesn’t have to be great yet; the smallest-scope publishable version is enough. What L5 says about your career is that the only failure mode is not having a next paper. The calendar entry is how you make sure you do.

1. Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854–856.

2. Radicchi, F., Fortunato, S., & Castellano, C. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. PNAS, 105(45), 17268–17272.

3. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

4. Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 56–63.

5. Petersen, A. M., Fortunato, S., Pan, R. K., Kaski, K., Penner, O., Rinaldo, A., Riccaboni, M., Stanley, H. E., & Pammolli, F. (2014). Reputation and impact in academic careers. PNAS, 111(43), 15316–15321.

6. Algaba, A., Mazijn, C., Holst, V., Tori, F., Wenmackers, S., & Ginis, V. (2025a). Large language models reflect human citation patterns with a heightened citation bias. In Proceedings of the 2025 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 6844–6853). Association for Computational Linguistics.

7. Algaba, A., Holst, V., Tori, F., Mobini, M., Verbeken, B., Wenmackers, S., & Ginis, V. (2025b). How deep do large language models internalize scientific literature and citation practices? arXiv preprint 2504.02767.

8. Wieczorek, O., Steinhardt, I., Schmidt, R., Mauermeister, S., & Schneijderberg, C. (2024). The bot delusion: Large language models and anticipated consequences for academics’ publication and citation behavior. Futures, 166, 103537. See also Baert, P., Dorschel, R., Hall, M., Higgins, I., McPherson, E., & Philip, S. (2025). Dialogues towards sociologies of generative AI. Social Science Computer Review (online first).

9. Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.

10. Shen, H.-W., & Barabási, A.-L. (2014). Collective credit allocation in science. PNAS, 111(34), 12325–12330.

11. Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A.-L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239.

Part II: What you can actually do this week

Nine tactics. Each one is short. Each one is anchored to one or more of the five laws. Each one ends with a “do this today” checklist. Do them in order. The order is roughly “easiest with highest leverage” first. If you only do tactic 1 (profile hygiene) and tactic 2 (post the preprint), you have already done more than most of your colleagues.

At a glance

# | Tactic | Time | Anchored to
1 | Make your work findable by machines | 2h once | L1, L3
2 | Post the preprint | 10 min / paper | L3
3 | Go open access when you can | varies | L3
4 | Write the plain-language summary | 30 min / paper | L1, L4
5 | Email the people whose work you cited | 30 min / paper | L1, L3, L4
6 | Reconnect with five dormant ties each quarter | 1 email / week | L1, L3
7 | Show up where the conversation actually is | 1 answer / month | L1, L2, L3
8 | Pick a recurring talk and recycle it | 4–6 talks / year | L1, L3, L5
9 | Don’t quit | every year | L5

Tactic 1. Make your work findable by machines

If your paper does not surface when ChatGPT, Perplexity or Google AI Overviews are asked about your topic, your paper does not exist for a growing fraction of your future citers. Profile hygiene is the cheapest, highest-leverage visibility action available to you.

This is unsexy. Do it anyway. It is a one-afternoon job that pays for the rest of your career.

The mechanism: LLM-based search engines build their answers from publicly indexed text. They love structured author profiles (ORCID, Google Scholar, OpenAlex), plain-language summaries (a personal site, a LinkedIn “About” section, a paragraph on your institutional page), and consistent author-name disambiguation. They hate the situation where you publish as “B. Gorelik” in one paper and “Boris A. Gorelik” in another and “Gorelik B.” in a third. They will treat you as three different people. They will undercount your work.

The laws this leverages: L1 (when the field is too big for any one human to track, machines mediate recognition) and L3 (a clean profile lets the Matthew effect accumulate to you instead of to three confused half-yous).

Most of this tactic is mechanical: claim profiles, paste papers, fix the name spelling. The one piece of actual writing is the 2-3 sentence bio that goes on your institutional page, your ORCID, your LinkedIn. Don’t write it from scratch. Feed your top 3 papers and a one-sentence “what I’m working on now” into Template 9 (Part III). Edit the draft until it sounds like a description, not a brag, then mirror it word-for-word across every page.

Do this today:

Profile hygiene checklist

Create or claim your ORCID iD. Add every paper. Pin your top 5.

Claim your Google Scholar profile. Enable email alerts for new citations. Pin your top 5.

Claim your OpenAlex author record. Verify the works list. Merge duplicate author IDs if any.

Pick one canonical spelling of your name and use it on every future submission. Make sure your ORCID and Scholar and OpenAlex agree.

Run Template 9 (Part III) to draft your 2-3 sentence plain-English bio. Edit the output. Add it to your institutional page.

Mirror that bio, word-for-word, on your personal site, your LinkedIn “About”, and the Notes field of your top ORCID/Scholar entries. Repetition is what makes a phrase canonical to an LLM.

Spend two hours on this. You will be done forever. (Approximately. Update annually.)

Tactic 2. Post the preprint

Preprints get cited earlier and more. The reflex to “wait until acceptance” is costing you citations. Stop doing it.

The peer-reviewed literature on this is now very clear. Papers posted to arXiv, bioRxiv, SSRN, PsyArXiv or the appropriate disciplinary preprint server accumulate citations earlier than journal-only versions, and across many fields they accumulate more total citations.¹ The mechanism is not subtle: papers that exist 6–18 months earlier are available to be cited 6–18 months earlier, and citations beget citations (L2, L3).

The preprint citation premium

Earlier and higher: across the major preprint servers studied, papers posted as preprints accumulate citations sooner and reach a higher long-run citation count than otherwise-comparable papers posted only on journal acceptance. The effect is strongest in fast-moving fields (biology, physics, ML). Conclusion: posting the preprint is the single highest-ROI action available to a researcher who has a paper-in-progress. Source: Larivière, V., Sugimoto, C. R., Macaluso, B., Milojević, S., Cronin, B., & Thelwall, M. (2014). arXiv e-prints and the journal of record: An analysis of roles and relationships. Journal of the Association for Information Science and Technology, 65(6), 1157–1169. See also Fraser, N. et al. (2020). Preprinting the COVID-19 pandemic. bioRxiv, doi:10.1101/2020.05.22.111294 (an extreme-case demonstration of the citation-timing effect).

The objections people offer to preprinting are mostly mistaken:

“What if it doesn’t get accepted?” Then you revise and post the revision. You haven’t lost anything; you have generated 12 months of pre-acceptance visibility.

“What if I get scooped?” Posting the preprint is what prevents the scoop, because the timestamp is public and citable.

“My journal doesn’t allow preprints.” Most do. Check the journal’s open-access policy at sherpa.ac.uk/romeo. For the small minority that don’t, decide whether the journal is worth the visibility tax.

Version it. Every major preprint server (arXiv, bioRxiv, medRxiv, SSRN, PsyArXiv, OSF) supports replacement versions. Most researchers post v1 and never revisit. The no-risk cadence every researcher can adopt: v1 at submission, v2 when you respond to reviewers, v3 at acceptance. Each version is its own citation event for the indexers and a fresh signal to the people watching your work. As a side effect, posting v1 publicly is a commitment device — your future self is now answerable to a real, timestamped, citeable artifact, which is a surprisingly effective antidote to “I’ll polish it one more weekend”.

The advanced move: drafting in public. In arXiv-native fields (physics, math, parts of CS and ML) a stronger version of this tactic is to post the preprint well before journal submission — sometimes as a clearly-marked draft. Senior figures who do this include Terry Tao (blog and arXiv), Tim Gowers (Polymath project), and Stephen Wolfram (live science streams; see the Wolfram profile in “Why this handbook”). The upside is much longer exposure plus feedback before formal review. The downside is field-specific: in some biomedical, chemistry, social-science, and humanities sub-fields, posting visibly-draft work is still treated as unprofessional. Read your field before adopting this. The handbook does not recommend it as default; it recommends it as a deliberate choice if you know your field tolerates it.

Do this today:

Preprint checklist

Identify the appropriate preprint server for your field (arXiv for math/CS/physics; bioRxiv for biology; medRxiv for clinical; SSRN for social science; PsyArXiv for psychology; OSF Preprints as fallback).

Post your most recent in-submission paper there as v1 if it isn’t already.

Add the preprint DOI to your ORCID, your Scholar profile, and your CV.

Adopt the v1-at-submission / v2-at-reviewer-response / v3-at-acceptance cadence as your default for every future paper.

Decide, deliberately, whether your field tolerates posting a pre-submission draft. If yes, that becomes your v0.

Tactic 3. Go open access when you can

Open-access papers are cited more. The effect is small per paper, large over a career, and free if you choose the right routes.

Piwowar and colleagues (2018) ran the most thorough analysis to date of the open-access citation advantage.² They controlled for the obvious confounds (better papers tend to go OA, OA is more common in some fields) and the effect survived.

The OA citation premium

~18%: across all disciplines, open-access articles are cited approximately 18% more than otherwise-comparable paywalled articles. Conclusion: choose the OA option when you can afford it. When you can’t, post the green-OA version (author’s accepted manuscript) to your institutional repository or your personal site. Source: Piwowar, H., Priem, J., Larivière, V., Alperin, J. P., Matthias, L., Norlander, B., Farley, A., West, J., & Haustein, S. (2018). The state of OA: A large-scale analysis of the prevalence and impact of Open Access articles. PeerJ, 6, e4375.

A practical taxonomy: gold OA is publication-time OA paid for by an article processing charge. Green OA is self-archiving the author’s accepted manuscript on a repository or personal site. Bronze OA is “free to read on the publisher’s site, but the rights are murky”. Diamond OA is OA with no APC paid by the author; these are usually society or institution-backed journals and they are wonderful.

Green is the most under-used route. Most publisher contracts allow you to deposit the author’s accepted manuscript (the post-peer-review text without the publisher’s typesetting) to your institutional repository after an embargo period. Almost nobody does. You should.

Do this today:

Open-access checklist

For your most recent published paper, check your publisher’s green-OA policy at sherpa.ac.uk/romeo. If green-OA is allowed (it almost always is), deposit the author’s accepted manuscript in your institutional repository today.

If you don’t have an institutional repository, deposit it on Zenodo (free, archival, gives you a DOI).

When choosing where to submit your next paper, factor in OA route. A high-impact gold-OA journal pays for itself in citations.

Tired of doing this manually? Loud Camel runs all nine tactics for you on a recurring schedule. https://loudcamel.com

Tactic 4. Write the plain-language summary

A 200-word plain-English summary of every paper, posted alongside it, is the single highest-leverage piece of writing you can do this year. It is the version of your work that LLMs will quote, that reviewers will skim, and that journalists will paraphrase.

Your abstract is not a plain-language summary. Your abstract is a structured 250-word document optimized for fellow specialists in your sub-sub-field. It is full of jargon, conditional clauses, and the word “moreover”.

A plain-language summary is a different artifact entirely. It answers four questions in roughly four paragraphs:

What did we ask? In one sentence, the question, phrased so a smart non-specialist understands why anyone cares.

What did we do? In one or two sentences, the method, at a level of abstraction your grandmother would tolerate.

What did we find? In two or three sentences, the main result. With one number if you have a number.

So what? One sentence on why someone outside your sub-sub-field should care.

That’s it. Maybe 200 words.

The 30-minute number assumes you write from scratch. Don’t. Feed the abstract to an LLM with Template 2 in Part III, and you’re editing a draft in five minutes instead of writing one in thirty. This is what I do for every new paper. The LLM produces the structure; you do the cognitive work that matters — verifying every claim against the abstract, cutting the jargon the LLM didn’t catch, adding the one number that matters most, and rewriting the “so what” sentence so it reads like you and not like a model. Read the draft adversarially: if a sentence is not supported by the abstract, cut it.

Where does it go? Everywhere. - The first paragraph of your personal site’s “Publications” page (next to or instead of the abstract). - The “Notes” field of your ORCID entry for that paper. - The caption under your figures when you post them to social media or a blog. - The first 200 words of the discussion post you write about the paper (see Tactic 7). - The first paragraph of the email you send to people whose work you cited (see Tactic 5). - The press release, if your university press office wants one (they will be grateful; you have done their job for them).

The visual companion: a graphical abstract. Humans are visual creatures. A single image is digested faster than a 200-word paragraph, and it survives the social-media scroll in a way that prose does not. Once you have the plain-language summary, use an LLM to draft a graphical abstract in two versions: one aimed at peers in your sub-field (technical detail, one bold number, your contribution visible against prior work), and one aimed at a general audience (sequential narrative, plain captions, a “why this matters” opener in the first panel). Treat the LLM output as a layout brief — the actual rendering still happens in Figma, BioRender, Canva, or your image-generation tool of choice. Publish the peer version on your publications page and send it to potential collaborators. Use the public version for social media, the press release, and the first follow-up email to anyone outside your sub-field. Two prompts in Part III (“Graphical abstract prompts”).

The laws this leverages: L1 (LLMs and non-specialists are part of the network that recognizes you; the text and the visual are how you reach both) and L4 (these artifacts are how a middle-author contribution becomes findable separately from the paper itself).

Do this today:

Plain-language summary & graphical abstract checklist

For your most-cited paper, run Template 2 (Part III) on the abstract. Read the draft adversarially. Edit until every claim is in the abstract and the “so what” sentence is yours.

Paste the final version onto your personal site, into your ORCID entry, and into your Scholar bio.

Run the two graphical-abstract prompts (Template 3, Part III) on the same paper. Generate the peer version; render it in Figma / BioRender / Canva / your tool of choice.

Generate the public version. Render it. Use it for social media, the press release, and outreach to non-specialists.

Do all of the above for your three next-most-cited papers this week.

Make “run Template 2 + Template 3, then edit” the third bullet of your every-new-paper checklist, alongside “submit” and “post the preprint”.

Tactic 5. Email the people whose work you cited

Every paper has a built-in list of 30–60 people who already care about your topic: the authors you cited. Most researchers never email any of them about a new paper. This is one of the most wasted opportunities in academic visibility.

You have a new paper. You cited Author X in section 2 because their 2021 paper laid the groundwork for your method. Author X has no idea that your paper exists. Author X is statistically very likely to be interested.

Sending Author X a one-paragraph email saying “I built on your 2021 result, I think you’ll want to see what we did with it, here’s the PDF” is the closest thing to a free citation that exists. It is also the closest thing to a free citation that almost nobody does.

The mechanism: the email creates a direct, contextual exposure that is far stronger than a passive citation. Author X now associates a face (or at least a name) with the new work. Author X is now more likely to cite your paper in their next paper, mention you in a talk, suggest you as a reviewer, or invite you to a workshop. None of this is guaranteed; all of it is much more likely than if you said nothing.

If you posted the preprint before journal submission and emailed the cited authors at the same time (Tactic 2), this same mechanism extends to peer review. Journals routinely draw reviewers from a paper’s reference list, and a reviewer who has already seen your paper — through a no-ask, value-providing email — starts the review with a positive prior. This is not gaming the system. Conflict-of-interest rules still apply, and the no-asks principle from this tactic is the line. Email no one you would be embarrassed to have as a reviewer. Send no email that contains a request. Those two constraints are what make this honest outreach instead of manipulation.

The laws this leverages: L1 (the network produces recognition; you are deliberately wiring an edge into the network), L3 (a seed citation from a credible figure feeds the preferential-attachment loop), and L4 (especially if you are middle-author, this is how you become visible on the paper instead of being a credit-line statistic).

Do not ask for anything. Do not ask for a citation. Do not ask for “any feedback”. Provide value: a paper they will be glad they read.

Template 1 in Part III has both a fill-in version and an LLM prompt. The LLM mode is the faster path: paste in the cited paper’s title, your paper’s plain-language summary, and the specific use of their result, and you get a personalized first draft in a minute. Edit it. The key edit is always the same: make at least one sentence sound like you, not like a model.

Do this today:

Build-on-your-work outreach checklist

Pick one recently published paper of yours.

Make a list of the 5–10 most relevant authors you cited (not “famous people in the field”, but the most-relevant citations).

Run Template 1’s LLM prompt mode (Part III) for each recipient. One minute per email. Edit each draft to add one personal sentence.

Send all five today. Total time: 30 minutes.

Make this a default action for every future paper.

Tactic 6. Reconnect with five dormant ties this quarter

The most valuable people in your network are not your current collaborators. They are the former group-mates, department colleagues, cohort peers, conference acquaintances and old co-authors you have lost touch with. Reactivating them is high-leverage and almost effortless if you have a real “why now”.

Mark Granovetter’s strength of weak ties paper from 1973 has been cited about 70,000 times for a reason.³ Strong ties (your current collaborators, your immediate research group, your department) already know what you are working on. Weak ties (the people you knew once and don’t anymore) bring you new information, new opportunities, citations from outside your immediate circle.

But weak ties go dormant. You don’t keep up. They don’t keep up. After three years of silence, the activation energy to re-engage feels enormous. It isn’t, but it feels it.

The trick to reactivating dormant ties is the why now. A generic “let’s catch up” email is awkward and low-conversion. A specific, contextual email is genuinely welcome. For example: “I saw your 2024 paper on X; we worked on something adjacent in 2018; here is what we did, here is why your new result is interesting in that light.”

Template 5 in Part III gives you both the structural patterns and an LLM prompt mode. The LLM mode is the right default: paste in how you know the person, the why-now, and what you’re working on, and you get a personalized draft. Edit it. Crucially, the prompt is built to refuse if the why-now is generic — if it doesn’t have a real reason this email is happening this week, wait until you do.

The laws this leverages: L1 (weak-tie network = network mediation of recognition) and L3 (each reactivated tie is a potential seed citation).

Do this today:

Dormant-tie reactivation checklist

Make a list of 5 people in your wider network you have not been in regular contact with for 12+ months. Sources: past co-authors, conference acquaintances, former group-mates, PhD-cohort peers, your undergrad or master’s research supervisor, workshop attendees you once exchanged emails with. (Recent PhDs whose only co-authors are people you see weekly: focus on the non-lab network — conference acquaintances and cohort peers are your starting pool.)

For each, find a why now: a recent paper of theirs you can comment on, a topic they work on that connects to your current work, an event coming up that you both might attend.

Run Template 5’s LLM prompt mode (Part III) per recipient. Edit each draft. Send one per week for the next five weeks.

Set a calendar reminder to do this every quarter for the rest of your career.

Boris writes weekly on data, careers, and academic visibility. Subscribe to Direction Matters:
https://directionmatters.substack.com

Tactic 7. Show up where the conversation actually is

Pick one venue where researchers in your sub-sub-field already ask questions in public. Answer one question per month. Do this for two years. This is the single most underrated route to AI-search visibility.

Your sub-sub-field has a venue. It is most likely a subreddit (r/MachineLearning, r/AskAcademia, a discipline-specific subreddit), a Discord server, a discipline mailing list, a journal-club Slack, or a conference’s “discussion” channel — these are where the live conversation happens now. It may also be a StackExchange site (CrossValidated for stats, MathOverflow, Physics SE, Academia SE). Active question volume on SE has dropped substantially since ChatGPT launched, but that cuts two ways. SE answers remain heavily indexed by Google and disproportionately ingested by LLM training sets, so a good SE answer is one of the longest-lasting public artifacts you can produce. And with fewer competing answers per question, a thoughtful new contribution is more likely to become the canonical answer for that question — the low-competition dynamic that makes being early on any platform valuable, applied retroactively to a mature one. The trade-off is real: subreddits give you readers this week; SE gives you readers, and LLM citations, for years.

Find it. Lurk for two weeks. Identify a recurring question whose answer is partly what you wrote your last paper about.

Write the answer. Use Template 7 in Part III: feed in the public question, your paper’s plain-language summary (from Tactic 4), and which sub-question your paper actually answers. The prompt is built to write the answer first, mention your paper last, and demonstrate honesty by naming an open question your paper does not answer. Edit the draft. The opening paragraph must answer the question regardless of whether the reader ever clicks the link to your paper. The link is the cherry, not the cake.

Do this once a month. That is twelve high-quality public artifacts a year, each one visible to AI search engines, each one findable for years, each one a tiny seed in the Matthew loop.

The laws this leverages: L1 (you are now in the network where recognition happens), L2 (early citations of your paper now have a higher base rate of arrival), L3 (every answer with your name on it adds to the visible reputation signal).

Why this beats Twitter / X / LinkedIn: ephemerality. A tweet about your paper has a half-life of ~3 hours. A StackExchange answer about your topic is findable, often as the top Google result, for years. LLMs ingest those answers. They do not ingest tweets at anything like the same rate.

Do this today:

Public-conversation checklist

Identify the one venue where your sub-sub-field actually asks questions in public. (Not where they post. Where they ask.)

Lurk for two weeks. Note which questions recur.

Pick one recurring question whose answer touches on your work. Run Template 7 (Part III) with the question + your plain-language summary + the sub-question your paper answers. Edit the draft until the opening paragraph stands alone.

Schedule “answer one question this month” as a recurring monthly calendar item for the next year.

Tactic 8. Pick a recurring talk and recycle it

The same 30-minute talk, given 4–6 times a year to different audiences, beats a new talk every time. The audience changes. The message compounds.

You give the talk for the first time at your own research-group meeting. The audience is forgiving; they will tell you which parts are confusing. You polish. You give it at your department seminar a month later, and faculty will tell you which parts are under-developed. You polish again. You give it at a peer institution’s seminar series or a regional workshop. By the time it lands at a conference or an invited national talk, it is genuinely sharp — and the audience that matters most for your career sees the version that has been refined three or four times, not the first pass. The ordering principle: when you have the choice, give the talk to the smallest, most-forgiving audience first and the highest-stakes audience last. You will not always have the choice; invitations arrive when they arrive. But when you do have it, optimize for which audience sees the polished version.

Researchers who treat every talk as a from-scratch effort are quietly paying a huge tax. Researchers who pick one or two “calling-card” talks and give them many times in a year are not lazy; they are applying the same Salganik feedback loop to their own message that the field applies to papers. The talk gets sharper. The slides get sharper. The reactions get sharper. The audience for the next invitation is sharper, because the message has been refined enough that it travels.

When a new venue invites you, or when you see a call for presentations that fits, do not rewrite the talk. Run Template 8 in Part III: paste in your current talk’s abstract and the new audience (their field, their seniority, what they already know, what they don’t). The prompt returns a revised abstract for the new audience, three bridge slides to add at the start, one slide to remove, and one closing-slide sentence that connects your work to a question this audience cares about. Implement those four edits and keep the rest of the talk. The whole adaptation takes thirty minutes instead of two weeks.

The laws this leverages: L1 (the talk is the social-network version of your paper; rehearsing it for many audiences is how you propagate it), L3 (every venue you give it at increases the visibility signal), L5 (one of your future high-impact moments is statistically very likely to be a talk; you cannot predict which; give a lot of talks).

Do this today:

Recurring-talk checklist

Pick the one paper or research direction you most want to be known for in the next 12 months.

Prepare a 30-minute talk on it. Plain-language opening (the Tactic-4 summary works perfectly here). One striking figure. One clear question you can answer with data. One clear question you can’t yet.

List 6–8 venues in order of audience size and stakes (research-group meeting → department seminar → peer-institution seminar / regional workshop → conference / national talk). When you have the choice of order, give the talk in that order.

Email the organizers of two of them this week. Use the talk title and the plain-language summary in the proposal.

When a new invitation arrives or a relevant call for presentations opens, run Template 8 (Part III) for the 30-minute audience adaptation. Apply the four returned edits and keep the rest of the talk.

Tactic 9. Don’t quit

Statistically, your single biggest paper has not yet been published. This is true whether you are 32 or 62. The only thing that guarantees you don’t have a breakthrough paper is that you stop writing them. (See Chapter 5.)

This isn’t a tactic. It’s a frame.

The L5 result (the random impact rule from Sinatra et al.) says your largest-impact paper is statistically uniformly distributed across your productive years. The thing that determines whether it happens at all is whether you are still publishing when the lottery ticket comes up. There is no statistical age at which you become “too late”. There is also no statistical age at which you become “too early”.

The career-killer in the L5 frame is not “I didn’t have my breakthrough early enough.” The career-killer is “I quit before it arrived.” Many early-to-mid-career researchers (exactly the people this handbook is for) quietly decide, somewhere in year 5 of their independent career, that the breakthrough wasn’t going to happen and they should pivot. Some of those decisions are correct; many of them are made one paper too early.

Do this today:

The keep-going checklist

If you are currently between papers, identify the next one. Pick a date for the first submission draft. Put it in your calendar.

If you are currently mid-paper, identify the next-next one. Don’t wait until this one is submitted to start the next.

If you have not published in 18 months, this is the only emergency in this handbook. Address it. Smaller-scope papers count.

You haven't quit. Loud Camel compounds the work — surfacing new collaborators, dormant ties, and outreach drafts on a schedule. https://loudcamel.com

1. Larivière, V., Sugimoto, C. R., Macaluso, B., Milojević, S., Cronin, B., & Thelwall, M. (2014). arXiv e-prints and the journal of record: An analysis of roles and relationships. Journal of the Association for Information Science and Technology, 65(6), 1157–1169.

2. Piwowar, H., Priem, J., Larivière, V., Alperin, J. P., Matthias, L., Norlander, B., Farley, A., West, J., & Haustein, S. (2018). The state of OA: A large-scale analysis of the prevalence and impact of Open Access articles. PeerJ, 6, e4375.

3. Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.

Part III: Templates and ChatGPT prompts

Copy-paste material. Each template ends with the same caveat: review every output before sending or publishing. The system in your hand is a draft generator, not a sender.

One reason this matters more than it sounds. The Book of Ecclesiastes is unambiguous: tov shem mi-shemen tov — “a good name is better than fine oil” (Kohelet 7:1). Nassim Taleb framed the same idea in modern terms in Skin in the Game: your name on the work is your skin in it. The drafts these templates produce go out under your name. They reach people whose review of a grant, a paper, a job application, or a collaboration request you may one day depend on. Do not trust anyone or anything to sign your name. Do not send or publish anything you have not read to the last letter. The LLM does not bear the cost of an embarrassing email. You do.

[picture]

Loud Camel

About Loud Camel

The handbook you are reading describes nine tactics. Loud Camel is the system that runs the tactics for you on a recurring schedule. It identifies new scholars whose work connects to yours (Tactic 5), surfaces dormant contacts worth reactivating (Tactic 6), drafts the outreach emails (also Tactics 5 and 6), drafts the public-discussion posts (Tactic 7), and audits whether AI search engines surface your work (a continuous version of Tactic 1). It never sends anything on your behalf. You review every draft.

Start your alpha access: https://loudcamel.com

Template 1. “Hi, I built on your work” outreach email

When to use: You have a new paper. You cited Author X. You want Author X to know your paper exists.

Adapt: - One sentence on the specific result of theirs you used. - One sentence on what you did with it. - One link to the paper (PDF or DOI).

Template:

Subject: I built on your <YEAR> <TOPIC> result. Thought you'd want to see what came of it

Hi <FirstName>,

I'm <Your Name>, <position> at <institution>. I wanted to drop you a quick note because your <YEAR> paper on <SPECIFIC RESULT> was a load-bearing reference in something I just published.

Briefly: we <ONE-SENTENCE METHOD> and found that <ONE-SENTENCE FINDING>. The connection to your work is in <SECTION X / FIGURE Y>, where we use your <SPECIFIC RESULT> to <WHAT IT ENABLED>.

PDF / preprint: <LINK>

Not asking for anything; just thought you'd want to see it. Happy to discuss if any of it is useful to your current work.

Best,
<Your Name>

Variants by relationship distance:

Cold (no prior contact): as above.

Warm (you’ve met at a conference): add one sentence, like “We crossed paths at <CONFERENCE>, <YEAR>. You may not remember; I was the one who asked the question about <TOPIC>.”

Reactivated co-author: “I know it’s been a while since we worked on <OLD PROJECT>. I wanted to share something that finally closed a thread we started back then.”

Senior figure you’ve never met: the template above. Do not pretend to know them. The “not asking for anything” line is doing real work.

LLM prompt mode (the faster path):

You are drafting a one-paragraph academic outreach email on my behalf. The recipient (Author X) wrote a paper that I cited in section <SECTION> of my new paper. I want to tell them my paper exists, without asking for anything.

Inputs:
- Their cited paper title + 1-sentence summary: <PASTE>
- My new paper title + plain-language summary (from Template 2): <PASTE>
- My specific use of their result in my paper (one sentence): <PASTE>
- My link (PDF or preprint): <PASTE>
- Relationship: cold / met-once-at-conference / former-co-author / senior-figure-I-have-not-met

Draft an email that:
1. Has a subject line that signals the content (not "checking in" or "quick question").
2. Opens with one sentence stating who I am and that I cited their work.
3. Has two short paragraphs: what I did, and where their result fits in.
4. Ends with "Not asking for anything; just thought you'd want to see it. Happy to discuss if any of it is useful to your current work."

Hard constraints:
- No marketing language. No "groundbreaking", "novel", "exciting".
- No requests. Do not ask for a citation, feedback, or a meeting.
- Total length: 120–180 words including subject and signature.
- Adapt the opening to the relationship: cold = formal first-line introduction; warm = reference the prior encounter in the first or second sentence.

Caveat: review every email. The LLM produces a generic-sounding first draft; the cheapest edit that lifts response rate is to add one sentence that only you could have written — a specific reaction to their paper, a detail about your method, a sentence in your own voice. A generic “Dear Professor X” is worse than no email at all.

Template 2. Plain-language summary prompt

When to use: You have an abstract. You need the 200-word plain-language summary for Tactic 4.

Prompt:

You are an editor for a general-science magazine. You will receive the abstract of a peer-reviewed paper.

Rewrite the abstract as a 200-word plain-language summary, structured in four short paragraphs:

1. The question (1–2 sentences, phrased so a smart non-specialist understands why anyone would care).
2. The method (1–2 sentences, at a level of abstraction a curious 16-year-old can follow).
3. The finding (2–3 sentences. If the abstract gives a quantitative result, include the number).
4. So what (1 sentence on why someone outside this sub-sub-field should care).

Hard constraints:
- No jargon. If a term is unavoidable, define it inline in one short clause.
- No words like "moreover", "furthermore", "interestingly", "novel". They add nothing.
- No hedging beyond what the abstract itself contains.
- 180–220 words total.
- Plain declarative sentences. Active voice when possible.

Abstract:
<PASTE ABSTRACT HERE>

Caveat: the output is a draft. Read it as if you were a colleague in a different sub-discipline. Cut anything that still sounds like jargon to you in that hat. Add the one number that matters most. Add the one sentence that makes the so what personal to a likely reader.

Stop drafting these by hand. Loud Camel runs the recurring outreach for you. https://loudcamel.com

Template 3. Graphical abstract prompts (peer + public)

When to use: You have a plain-language summary (Template 2). You want a visual companion in two versions: one for peers in your sub-field, one for a general audience. The LLM does not draw the image; it produces a layout brief you implement in Figma, BioRender, Canva, or pass to an image-generation model as the design spec.

Procedure: Run Prompt A. Take the layout into your tool. Run Prompt B. Render. Post both versions in the right places (peer version on the publications page and to potential collaborators; public version on social media, in the press release, and to non-specialists).

Prompt A — peer-audience graphical abstract:

You are a science illustrator preparing a graphical abstract for an academic audience in <FIELD/SUB-FIELD>.

I will give you the abstract of my paper. Propose a graphical abstract: a single-image specification a designer can implement in Figma, BioRender, or Adobe Illustrator.

The specification must include:
1. Layout structure (boxes, arrows, flow direction) in 3 to 5 sentences.
2. The text in each box, kept to ~10 words or less per box.
3. The key visual anchor: which figure or concept from the paper should be the centerpiece.
4. One bold number (the key result) that should be visually prominent.
5. One sentence on how this graphical abstract differs from or extends prior work, so a reader who knows the field can grasp the contribution in 5 seconds.

Hard constraints:
- The graphical abstract must be readable at thumbnail size (mobile, social media preview).
- Use technical terms from the field where they aid clarity; do not over-define.
- No marketing language. No "novel", "groundbreaking", "innovative".

Abstract:
<PASTE ABSTRACT HERE>

Prompt B — general-public graphical abstract:

You are a science illustrator preparing a graphical abstract for a general audience: a curious non-specialist, a journalist, a grant officer, a PhD student in a different field.

I will give you the plain-language summary of my paper. Propose a graphical abstract: a single-image specification a designer can implement in Figma, Canva, or an image-generation tool.

The specification must include:
1. A sequential, narrative layout (left-to-right or top-to-bottom) that an outsider can follow in 10 seconds.
2. Plain-language captions for each panel, at the reading level of a smart 16-year-old.
3. One striking visual hook in the first panel (the "why should I care" element).
4. One bold takeaway in the final panel (the "so what" sentence from the plain-language summary).
5. A guidance note on color, mood, and any analogy or metaphor that helps the non-specialist grasp the topic.

Hard constraints:
- No jargon. If a technical term is unavoidable, define it inline in plain English.
- No academic-paper aesthetics (no Greek letters, no PNG-of-a-LaTeX-equation, no overlapping bullet lists).
- The first thing the viewer sees must answer "why does this matter to me?".

Plain-language summary:
<PASTE PLAIN-LANGUAGE SUMMARY FROM TEMPLATE 2 HERE>

Caveat: the LLM produces a layout brief, not the image itself. The actual rendering is on you (Figma / BioRender / Canva / Affinity / DALL-E / Midjourney / Stable Diffusion). Iterate: the first pass is rarely the final one. Both versions should survive a thumbnail-size readability test before you publish them.

Template 4. “Reviewer-readable” abstract prompt

When to use: You suspect that a grant reviewer outside your sub-sub-field could not, in 30 seconds, place where your contribution sits in the field. This is a separate prompt from the plain-language summary. This one keeps technical precision but optimizes for fast field-placement.

Prompt:

You are a grant-panel reviewer who is a respected researcher in a field adjacent to, but not the same as, the field of the abstract you are about to receive.

Rewrite the abstract so that *you* could understand it in 30 seconds. You may keep technical terms that are well-known one field over. You must define or replace any term that is only known inside the sub-sub-field.

Structure:
1. One sentence on the question, phrased to connect to a broader body of work the reviewer would know.
2. One sentence on the method, named by its broader family (e.g. "a regression-discontinuity design", "a transformer-based encoder", "a randomized double-blind trial") rather than its sub-sub-field name.
3. Two to three sentences on the result, including the single most important number.
4. One sentence on the implication for the broader field, not just for the sub-sub-field.

Total length: 150–200 words. No marketing language. No "novel", "innovative", or "groundbreaking".

Abstract:
<PASTE ABSTRACT HERE>

Caveat: if the rewritten abstract feels like it loses precision, that is the point. Reviewer-readable ≠ specialist-readable. Both versions should exist; this one goes in the grant proposal and the executive summary.

Template 5. Reconnection email templates

When to use: You have an old co-author, group-mate, department colleague, conference acquaintance, or PhD-cohort member you have not spoken to in 2+ years. You have a real why now (Tactic 6).

Template A: old co-author

Subject: <FIRST NAME>, something that closed a thread we started

Hi <FirstName>,

It's been a while. I came across <THEIR RECENT PAPER / RECENT MOVE / RECENT NEWS> and it reminded me of <SPECIFIC SHARED PROJECT, YEAR>.

I just finished a paper that <ONE SENTENCE connecting directly to the old project>. Thought you might find it interesting, especially in light of your recent work.

Link: <PDF / preprint>

Hope you're well. Would be glad to catch up if you're up for it. No agenda.

Best,
<Your Name>

Template B: conference acquaintance

Subject: We met at <CONFERENCE>, <YEAR>; thought of you reading <PAPER>

Hi <FirstName>,

We crossed paths at <CONFERENCE>, <YEAR>. We were both in <SESSION/PANEL/DINNER>. I read your <RECENT PAPER> over the weekend and it intersects with something I've been working on.

Briefly: <ONE-SENTENCE DESCRIPTION OF YOUR WORK AND THE INTERSECTION>.

Link to a preprint, if useful: <LINK>

Would enjoy a chat if you have 20 minutes in the next few weeks. No agenda; your paper just made me think we should talk.

<Your Name>

Template C: former group-mate or cohort peer

Subject: <FIRST NAME>!

Hi <FirstName>,

Embarrassingly long time. I keep meaning to write and not doing it.

<ONE SENTENCE ABOUT WHAT YOU'RE DOING NOW>. I saw <THEIR RECENT NEWS / PAPER> and wanted to say two things: first, congratulations. Second, <ONE SENTENCE ABOUT WHY THEIR NEWS CONNECTS TO YOUR CURRENT WORK>.

Would love to catch up. Coffee call, 30 minutes, whenever works for you.

<Your Name>

LLM prompt mode (the faster path):

You are drafting a short, contextual reconnection email on my behalf. The recipient and I have not been in touch for 2+ years. I have a specific "why now" — a real reason this email is happening this week, not just "let's catch up".

Inputs:
- How we know each other: <PASTE — old co-author / conference acquaintance / former cohort / former group-mate / department colleague / etc.>
- Last contact context (rough year, project, place): <PASTE>
- Why now (be specific — their recent paper / news / move; or a topic that became newly relevant): <PASTE>
- What I am working on now, in one sentence: <PASTE>
- Link to share if useful (preprint, paper, project): <PASTE OR LEAVE BLANK>

Draft an email that:
1. Has a subject line that names the why-now (not "long time" and not "checking in").
2. Opens with one sentence acknowledging the gap honestly.
3. Has one short paragraph delivering the why-now and the connection to my current work.
4. Closes with a no-pressure invitation to chat ("happy to catch up if you're up for it") or a clean signoff if no meeting is appropriate.

Hard constraints:
- No "I hope this email finds you well".
- No "just wanted to reach out".
- The why-now must do real work. If the inputs do not contain a real why-now, return only this sentence and stop: "Without a specific why-now, this email will read as awkward. Wait until you have one."
- Total length: 100–150 words including subject and signature.

Caveat: reconnection emails work when the why now is real and specific. They fail when the why now is generic (“just thought I’d reach out”). The prompt’s last hard constraint exists for exactly this case — if the LLM tells you to wait, listen.

More on outreach that lands, weekly. Subscribe to Direction Matters:
https://directionmatters.substack.com

Template 6. AI-search-visibility audit prompt

When to use: Once per quarter. You want to know whether ChatGPT, Perplexity and Google AI Overviews surface your work when asked about your topic.

Procedure:

List the 3–5 questions in your sub-sub-field that you would expect a smart non-specialist (a journalist, a grant officer, a PhD student outside your group) to type into ChatGPT or Perplexity.

For each question, run this prompt in both ChatGPT and Perplexity (and Google AI Overviews if your topic surfaces one):

I'm doing a literature scan on <TOPIC, IN PLAIN ENGLISH>.

Please give me the 5–10 most important researchers and papers I should know, with one sentence on why each one matters. Prefer recent work (last 5 years) where applicable.

Note: did your name appear? Did your most-cited paper appear? Did a paper of yours that should be in the answer fail to appear?

If your work is missing, the diagnosis is one of:

Profile hygiene failure (Tactic 1): your work exists but the LLM doesn’t connect it to your name. Action: clean up ORCID / Scholar / OpenAlex.

Plain-language failure (Tactic 4): your work exists in databases but not in indexable plain English. Action: write the plain-language summary, post it everywhere.

Conversation-presence failure (Tactic 7): your work has not been discussed in places LLMs ingest. Action: write the StackExchange / subreddit / blog answer that cites your paper.

Genuine novelty gap: your topic is too new for LLMs to have crystallized. Action: write the plain-language summary and the discussion post and wait 6–12 months.

Caveat: this is a noisy signal. Run it quarterly, not weekly. The trend matters more than any single result.

Template 7. Discussion-post prompt

When to use: You have a paper. You found a recurring question on a public venue (StackExchange, subreddit, mailing list, journal club forum). You want to write a thoughtful, non-spammy answer that links back to your work (Tactic 7).

Prompt:

You are a thoughtful, slightly cynical researcher who hates self-promotional content but loves explaining things clearly.

I will give you (a) a public question, (b) the abstract or summary of my paper, and (c) which sub-question of the public question my paper answers.

Draft a response that:
- Opens by answering the public question directly. Do NOT mention my paper in the first paragraph.
- In paragraphs 2–3, gives the substantive answer using plain language. Use a number if there is a number. Cite 1–2 well-known references that are not mine.
- In the final paragraph, says: "Full disclosure, I'm one of the people who worked on this. Here is our paper, which goes into <SUB-QUESTION>: <LINK>". Then mentions one open question my paper does NOT answer, to demonstrate honesty.

Total length: 250–400 words. No marketing language. No "we're excited to share". No "groundbreaking".

(a) Public question: <PASTE>
(b) My paper summary: <PASTE PLAIN-LANGUAGE SUMMARY FROM TACTIC 4>
(c) Sub-question my paper answers: <PASTE>

Caveat: these posts are findable for years. Make sure the answer is genuinely helpful even if a reader never clicks the link to your paper. The link is the cherry, not the cake.

Template 8. Conference-talk-recycling prompt

When to use: You have a 30-minute talk. You have been invited to give it at a different audience (different sub-field, different seniority, different country). You want to adapt without rewriting (Tactic 8).

Prompt:

You are helping me adapt a talk I already give well to a new audience.

I will give you:
(a) the abstract of my talk as it currently is,
(b) the new audience: their field, their seniority, what they already know, what they don't.

For the new audience, propose:
1. A revised one-paragraph abstract (~120 words) optimized for THIS audience.
2. Three slides that should be ADDED at the start to bridge from what they know to what my talk assumes.
3. One slide that should be REMOVED because it assumes background this audience lacks.
4. One sentence I should add to the closing slide that connects my work to a question THIS audience cares about.

Do NOT propose rewriting the whole talk. Reuse is the point.

(a) Current talk abstract: <PASTE>
(b) New audience: <DESCRIBE>

Caveat: the model is good at proposing the bridge slides. It is mediocre at choosing the slide to remove. Trust yourself on the cut, not the prompt.

Loud Camel runs the recurring half of all this on your behalf. You review every draft.
https://loudcamel.com

Template 9. Profile-bio prompt

When to use: You are doing the profile-hygiene work in Tactic 1 and need the 2-3 sentence “About” bio that goes on your institutional page, your ORCID Notes, your Google Scholar bio, and your LinkedIn “About”. Same text, mirrored across surfaces. This prompt drafts a version you then edit.

Prompt:

You are a science communicator helping me write the 2-3 sentence "About" bio that appears on my institutional page, ORCID, and LinkedIn. The bio must be plain English, no jargon, and tell a smart non-specialist what I work on and why it matters in <SUB-SUB-FIELD>.

Hard constraints:
- 2 to 3 sentences. ~50 words total.
- First sentence: what I work on, in plain English.
- Second sentence: one specific result, dataset, or method I am known for, or that I am building towards.
- Optional third sentence: why this matters to someone outside my sub-sub-field.
- No marketing language. No "passionate about", "expert in", "leading researcher", "groundbreaking". The bio should sound like a description, not a brag.

Inputs:
- My sub-sub-field: <PASTE>
- My top 3 papers (title + 1-line gist each): <PASTE>
- One sentence on what I'm working on now: <PASTE>

Caveat: the LLM will produce a bio that is technically correct and slightly bland. The cheapest edit that lifts quality is to rewrite the second sentence in your own voice — name the specific thing you are known for, with one concrete detail (a dataset name, a method, a number) instead of an abstraction. Then mirror the final bio word-for-word across every surface. The repetition is what makes the phrase canonical to an LLM-search engine.

If you only do two things

On the same day you submit a paper, do two things: (1) post the preprint, and (2) post a 200-word plain-language summary alongside it. That paired habit, repeated for the rest of your career, will outperform every other tactic in this handbook combined.

Most researchers wait for journal acceptance before doing anything public with a paper. That is 6–18 months of accumulated lost visibility per paper. Over a career, it is a substantial number of citations you will never get.

The pair of actions that breaks this habit is the one in the title above. Submit the paper. The same day, post the preprint. The same day, write the plain-language summary and put it on your personal site, your ORCID, your Scholar bio. The same day, email five of the people you cited (Tactic 5 template).

You will feel slightly exposed the first time. Then you will notice that the citations on your next paper accumulate slightly faster than they used to. Then you will keep doing it.

If this handbook bought you that pair of habits, it has paid for itself.

If you want a system that does the recurring work for you (the new-scholar discovery, the dormant-contact reactivation, the AI-search audit, the draft outreach emails), that is what Loud Camel does. It is built around exactly the tactics in this handbook. It never sends anything on your behalf.

Start your alpha access: https://loudcamel.com.

Boris Gorelik, PhD — founder. boris@gorelik.net.

References

Sorted by author. APA-style.

Algaba, A., Mazijn, C., Holst, V., Tori, F., Wenmackers, S., & Ginis, V. (2025a). Large language models reflect human citation patterns with a heightened citation bias. In Proceedings of the 2025 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 6844–6853). Association for Computational Linguistics.

Algaba, A., Holst, V., Tori, F., Mobini, M., Verbeken, B., Wenmackers, S., & Ginis, V. (2025b). How deep do large language models internalize scientific literature and citation practices? arXiv preprint 2504.02767.

Baert, P., Dorschel, R., Hall, M., Higgins, I., McPherson, E., & Philip, S. (2025). Dialogues towards sociologies of generative AI. Social Science Computer Review (online first).

Barabási, A.-L. (2018). The Formula: The Universal Laws of Success. Little, Brown and Company.

Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

Fraser, N., Brierley, L., Dey, G., Polka, J. K., Pálfy, M., Nanni, F., & Coates, J. A. (2020). Preprinting the COVID-19 pandemic. bioRxiv, doi:10.1101/2020.05.22.111294.

Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.

Larivière, V., Sugimoto, C. R., Macaluso, B., Milojević, S., Cronin, B., & Thelwall, M. (2014). arXiv e-prints and the journal of record: An analysis of roles and relationships. Journal of the Association for Information Science and Technology, 65(6), 1157–1169.

Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 56–63.

Petersen, A. M., Fortunato, S., Pan, R. K., Kaski, K., Penner, O., Rinaldo, A., Riccaboni, M., Stanley, H. E., & Pammolli, F. (2014). Reputation and impact in academic careers. PNAS, 111(43), 15316–15321.

Piwowar, H., Priem, J., Larivière, V., Alperin, J. P., Matthias, L., Norlander, B., Farley, A., West, J., & Haustein, S. (2018). The state of OA: A large-scale analysis of the prevalence and impact of Open Access articles. PeerJ, 6, e4375.

Radicchi, F., Fortunato, S., & Castellano, C. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. PNAS, 105(45), 17268–17272.

Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854–856.

Shen, H.-W., & Barabási, A.-L. (2014). Collective credit allocation in science. PNAS, 111(34), 12325–12330.

Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A.-L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312), aaf5239.

Wang, D., Song, C., & Barabási, A.-L. (2013). Quantifying long-term scientific impact. Science, 342(6154), 127–132.

Wieczorek, O., Steinhardt, I., Schmidt, R., Mauermeister, S., & Schneijderberg, C. (2024). The bot delusion: Large language models and anticipated consequences for academics’ publication and citation behavior. Futures, 166, 103537.

Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.

About Boris Gorelik

[picture]

Boris Gorelik, founder of Loud Camel.

I’m Boris Gorelik, PhD. I’ve spent two decades as a data scientist, across life sciences, cybersecurity, and social network analysis.

At 49, I returned to academia and faced a problem I hadn’t anticipated: my skills and track record were solid, but my scholarly visibility had eroded. The people who needed to know my work didn’t.

Rather than accept the gap, I applied the same tools I’d spent my career building (AI, network analysis, automated research pipelines) to the problem of scholarly presence. The results were immediate: relevant connections surfaced, outreach landed, a collaboration confirmed.

I built Loud Camel because the problem isn’t unique to me. Every researcher faces it. Most don’t have the technical background to solve it themselves. Now they don’t have to.

Loud Camel is currently in alpha with a small group of researchers. If you’d like to be one of them, loudcamel.com/h/signup is where you sign up. I read every email at boris@gorelik.net.

This handbook is version 2026-05-11. The version on the cover is the version you have. The current version is always at loudcamel.com/h/book.

© 2026 Boris Gorelik. This handbook is licensed under the Creative Commons Attribution-ShareAlike 4.0 International license (CC BY-SA 4.0). You may copy, redistribute, remix, and adapt the work for any purpose, including commercially, provided you give appropriate credit and license your derivative works under the same terms. License text: https://creativecommons.org/licenses/by-sa/4.0/.