That unease has a name and a number now. In November 2025, Bernhard Sabel and Dan Larhammar published a Perspective in Royal Society Open Science called “Reformation of science publishing: the Stockholm Declaration”. Larhammar is a past president of the Royal Swedish Academy of Sciences, so this is not a blog rant, it is a formal shot across the bow. Their message, stated flatly: fake and low-quality science is now being produced at industrial scale, and they call it “arguably the largest science crisis of all time, threatening to erode people’s trust in research.”

What the paper claims, in plain numbers

The authors name three threats to the literature. First, for-profit publishers, who charge steep fees for open access and take, by the authors’ own figure, between 25% and 35% profit from academia. Second, predatory journals, which publish a flood of low-quality papers with no real peer review. Third, and newest, paper mills.

The headline number is the number of fake papers: “hundreds of thousands per year,” which the paper attributes to two earlier studies. If that estimate is anywhere near right, the counterfeit is no longer a rounding error in the scientific record. It is a meaningful fraction of it.

What a paper mill actually is

A paper mill is a business. It manufactures articles with fabricated data, increasingly using AI-generated text, tables, and images, and then, in the paper’s own words, it “sells authorships to scientists under pressure to publish and bribes editors to publish these papers.”

The buyer is not a cartoon villain. It is usually a researcher who needs one more line on a CV to keep a job, land a grant, or finish a degree, working inside a “publish or perish” culture that counts papers instead of reading them. The paper’s point: the incentive comes first, the fraud follows. The metrics we chose, publication count, impact factor, h-index, get gamed, because any scoreboard eventually does.

What the Stockholm Declaration wants

The declaration was drafted at a conference at the Royal Swedish Academy of Sciences on 9-10 June 2025, and signed by more than two dozen researchers, including the image-integrity sleuth Elisabeth Bik and a board member of PubPeer. It asks for four things:

• Academia takes publishing back. Move to non-profit models (diamond open access, authors keep copyright) instead of paying for-profit publishers.
• Reward quality, not quantity. Stop scoring people by how many papers they produce, in hiring, tenure, and funding.
• Independent fraud detection. Detection run by “researcher-controlled” bodies that are not funded by publishers, able to “fraud tag” fake articles and the journals that print them.
• Legislation. Legal definitions and penalties for industry-scale fraud, so that paying a paper mill carries an actual cost.

My take: the diagnosis is sharper than the cure

I believe the diagnosis. The cure I’m less sure about.

The scary number does a lot of work, and it arrives as an estimate carried across a citation, not something I can audit from the page. I would have liked a table. The four recommendations, meanwhile, are the ones the reform movement has been making for years. The paper knows this: it prints its own table of predecessors, Leiden 2015, DORA 2012, Plan S 2018, and more. Good ideas usually don’t fail for lack of being restated one more time. There is even a reply already in the same journal, titled “Why the Stockholm Declaration will never work”, which argues the plan rests on assumptions that are too optimistic. Read it next to the declaration, not instead of it.

Two smaller notes, for honesty. The lead author founds and holds a stake in a science-integrity foundation, disclosed in the paper. That doesn’t make him wrong, it is context. And the paper carries a line I keep re-reading: “We have not used AI-assisted technologies in creating this article.” You have to declare that now. That’s the world we write in.

Why this is your problem, even if your work is clean

If you’re a researcher trying to be found and cited, and your work is honest, you might file this under someone else’s problem. It isn’t.

Fakes don’t only add noise, they tax trust, and the tax is flat. When a reader, a reviewer, or an AI answer engine can’t cheaply tell your real result from a manufactured one, the cheap move is to discount all of it. Skepticism gets applied to the whole field, and the honest pay it right alongside the frauds. The entire reason to put real work where people can find it is that being found is supposed to mean being believed. Erode the trust base and visibility stops paying: you can be the most discoverable paper in your subfield and still lose, if the currency of the field turns out to be counterfeit.

So maybe the check I started doing, is this paper real, isn’t paranoia anymore. Maybe it is just what reading looks like now. I would rather it went back to being paranoid. Do you think it’ll?

TL;DR: A Wikipedia citation is now an AI-visibility asset, because Wikipedia is one of the most heavily weighted sources in the data behind large language models. You can’t add your own work to get one: self-citation is a conflict of interest that editors watch for and revert. The honest path is to make your work the kind of source an editor reaches for, and to improve an article in your field without writing yourself into it.

Why does a Wikipedia citation matter more than its traffic?

Because the readers include the machines. Wikipedia shows up over and over in the corpora used to train large language models. It is a substantive part of Common Crawl and of The Pile, and it made up more than half of the data behind the original BERT model. In the GPT-3 training mix (Brown et al., 2020, Table 2.2), Wikipedia carried the highest sampling weight of any single source: tiny in raw size next to the web crawl, yet the model was pointed at it roughly 3.4 times, more than at anything else.

Read that again from your side of the desk. A sentence that cites your paper on a Wikipedia article isn’t just read by the people who land on that page. It is read, and reread, by the systems that increasingly answer questions for everyone else. If you care whether ChatGPT or Perplexity ever surfaces your work, this is one of the highest-value places for it to be mentioned. (Those systems already lean toward work that’s easy to find, which I wrote about in LLMs sharpen the Matthew effect.)

So why not just add the citation yourself?

Because Wikipedia treats that as a conflict of interest, and it does not go unnoticed. The relevant guideline, WP:SELFCITE, doesn’t flatly ban citing your own work, but it discourages it: keep it to a minimum, keep it in the third person, never give it undue weight, and when in doubt propose the edit on the talk page instead of making it yourself. Piling references to your own papers into an article is treated as a form of spam.

Two things make this a bad bet even when your work genuinely belongs there. First, every edit is public and permanently tied to your account in the page history, so “I added a citation to my own paper” stays on the record. Second, experienced editors patrol new citations and revert anything that reads as promotion. So the move that would do the most for your AI visibility is one you’re, by policy, not allowed to make directly. That is the whole problem. The rest of this post is how to get the result without breaking the rule.

What actually makes an editor reach for your work?

Editors cite sources that are easy to trust and easy to use. You make your work that source before anyone needs it:

• State the finding in one quotable sentence. An editor writing “studies have found X” needs a source that says X plainly. If your result is buried in hedged prose, it’s easier to cite the paper that said it clearly. (Same discipline as writing so AI search can read you.)
• Give it a stable, open link. A DOI that resolves, and a version anyone can actually read. Verifiability is the whole culture of the place; a paywalled PDF that a volunteer cannot open is a source they will skip.
• Earn secondary coverage. Wikipedia leans on secondary sources, a news write-up, a review article, a press piece that got picked up, more than on the primary paper by itself. Coverage is often what pulls a finding into an editor’s view in the first place, which is why a press release that actually lands does double duty here.

None of this is a trick. It’s the same thing that makes your work citable anywhere: a clear claim, a link that works, and someone other than you having noticed.

Can I touch a Wikipedia article in my own field at all?

Yes, and this is the version of it that works. Find an article in your area that’s thin or badly sourced, an unsupported claim, a “citation needed” tag, a statement propped up by a weak reference, and fix the sourcing with the best source available. Most of the time that source won’t be yours, and that’s fine. You’re improving the article, which is the point, and you’re learning where your field’s coverage is actually thin.

If your own work genuinely is the best source for a claim and nothing else fits, still don’t just insert it. Go to the article’s talk page, propose the specific edit, disclose that it’s your work, and let an uninvolved editor decide. That’s exactly what WP:SELFCITE tells you to do, and an edit that an independent editor accepts is one that sticks.

Where Loud Camel fits

This is slow, boundary-respecting work, and most of it’s knowing where your field’s conversation already points and where the sourcing is thin. That’s the kind of thing Loud Camel watches for you. It tracks where your work is already mentioned across the open web, flags where secondary coverage is missing (the gap that keeps you out of an editor’s reach), and drafts the outreach when there’s a real person worth contacting. It drafts; it never sends on its own. You stay the one who decides.

Common questions

What is the single best first step? Make one finding quotable and openly readable, then earn one piece of secondary coverage. That’s what moves your work into the pool editors actually draw from.

How long does this take to pay off? Longer than an edit you could make in a minute. That is the trade: the fast version gets reverted, the slow version stays.

The honest bottom line

The uncomfortable part is that the step that would help most here is one you’re not allowed to take yourself, so the work is indirect: be citable, be open, be covered, and let someone else make the edit. I could be wrong about how quickly the AI-visibility payoff arrives, since that part is still moving. The conflict-of-interest part is not an opinion, though. It’s policy, and the fastest way to get your citation deleted is to add it yourself.

Here’s the message, plain: if your paper doesn’t surface when ChatGPT, Perplexity, or Google AI Overviews are asked about your topic, your paper doesn’t exist for a growing fraction of your future citers. Profile hygiene is the cheapest, highest-value visibility action you’ve. It is unsexy. Do it anyway. It’s a one-afternoon job that pays for the rest of your career.

Why the machine cares about your profiles

LLM-based search engines build answers from publicly indexed text. They love structured author profiles (ORCID, Google Scholar, OpenAlex), plain-language summaries (a personal site, a LinkedIn About, an institutional-page paragraph), and consistent author-name disambiguation. They hate the three-spellings problem: they treat you as three people and undercount your work.

A clean profile lets recognition accumulate to you instead of to three confused half-yous. That is the whole game here. You are not optimizing an algorithm. You are giving the parser one consistent thing to attach credit to.

The checklist

Most of this is mechanical. Claim profiles, paste papers, fix the name spelling. Work down the list:

• 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.
• Pick one canonical spelling of your name and use it on every future submission. Make ORCID, Scholar, and OpenAlex agree.
• Draft your 2-3 sentence plain-English bio (prompt below). Edit it. Add it to your institutional page.
• Mirror that bio word-for-word on your personal site, LinkedIn About, and the Notes field of your top ORCID and Scholar entries.

Spend two hours. You are done (approximately, update annually).

The one piece of actual writing

The whole tactic has exactly one part that’s writing rather than clicking: the 2-3 sentence bio for your institutional page, ORCID, and LinkedIn. Don’t write it from scratch. Draft it with the prompt below, edit until it sounds like a description and not a brag, then mirror it word-for-word across every page. The repetition is what makes a phrase canonical to an LLM.

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 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>

The LLM bio will be correct and slightly bland. The cheapest fix is to rewrite the second sentence in your own voice, naming the specific thing you’re known for with one concrete detail. Then mirror it word-for-word.

Audit it every quarter

Once a quarter, check whether the AI engines actually surface you. List 3-5 questions a smart non-specialist (a journalist, a grant officer, a PhD student outside your group) would type into ChatGPT or Perplexity about your topic. For each, ask the engine for the 5-10 most important researchers and papers to know. Then note: did your name appear? Your most-cited paper? A paper that should be there but isn’t?

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

• Profile-hygiene failure. Clean up ORCID, Scholar, OpenAlex.
• Plain-language failure. Write and post the plain-language summary.
• Conversation-presence failure. Your work hasn’t been discussed where LLMs ingest text, so write the answer that cites it.
• A genuine novelty gap. Too new. Write the summary and the discussion post, then wait 6-12 months.

This is a noisy signal. One bad run means little. Run it quarterly and watch the trend, not the single result.

Start with ORCID

If you do one thing today, open ORCID, claim your iD, and add every paper under one spelling of your name. That is the cheapest hour in this whole guide, and the rest of the checklist gets easier once the canonical record exists.

This post is part of the Loud Camel field guide to academic visibility. You can read the whole guide as a single PDF: the handbook.

The list is real, and it is not junk. It comes from John Ioannidis and colleagues at Stanford, who built a set of science-wide author databases of standardized citation indicators out of Elsevier’s Scopus. The logic is reasonable. Raw citation counts are unfair to compare across fields, so they normalize them, fold six measures (total citations, three author-position citation counts, the h-index, and a co-authorship-adjusted h-index) into one composite, and publish the top slice inside each field. That slice is what everyone now calls the top 2%. It comes in two flavors, a single-year snapshot and a career-long one.

Before you print the certificate: in at least one field, the list tracks a US postal code about as closely as it tracks merit.

What the paper actually checked

A 2025 paper in the Journal of Informetrics took the 2024 edition of the list, picked one field, and counted. Péter Sasvári and Gergely Ferenc Lendvai, both at Ludovika University of Public Service in Hungary, chose legal studies, partly because “Law” is one of the fastest-growing fields on the list: since the first edition it has grown 811% in the career-long ranking and 491% in the single-year one. They pulled every law scholar from both rankings, 766 people in all (393 in the single-year list, 373 in the career-long), then scraped roughly 50,000 of their publications from Scopus to see who was citing whom.

The United States is heavily overrepresented. In the career-long ranking, 213 of 373 legal scholars were based in the US, about 57%. In the single-year ranking it was just under half, around 46%. The same person tops both lists: Cass R. Sunstein, whom the authors call “leagues above his peers.” The best-ranked non-US scholar, David Weisburd, sits far behind. In the single-year data Sunstein’s rank is 242 and Weisburd’s is 4,787. Across the board the US median rank beats the non-US one (131,187 against 163,484 in the single-year set, where a lower number is better).

Where they publish is most of the story

The gap is not really about who writes better law. It is about where the writing lands. The top 10 journals for US scholars are, all ten of them, American law reviews, and not one is published by a “Big 5” commercial house (Elsevier, Sage, Wiley, Taylor and Francis, Springer). They are the in-house reviews of elite law schools: Harvard, Chicago, Yale, Stanford, Penn. The Harvard Law Review is the single most popular venue, and articles there pull a mean of about 101 citations each.

Non-US scholars publish somewhere else entirely. Their top journals are European and international ones, with far thinner citation means. Their most common venue by article count is actually Nature, but those articles average just 5.39 citations. Not a single Global South journal appears in any of the top-journal tables. And when Sasvári and Lendvai mapped the citation network, US scholars mostly cited other US scholars, a dense, inward-looking web. The authors’ description of the ideal candidate for the list in law is worth quoting in full: “one has to fit three criteria: publish frequently in Ivy League universities’ journals, cite U.S. scholars, and it definitely does not hurt to be American either.”

A ranking that measures the neighborhood

Put the pieces together and, in this field, the ranking is not really measuring the scientist. It is measuring the neighborhood the scientist publishes in. Citations pool where the papers already cluster, the composite standardizes that pool, and the ranking hands the trophy back to where it started. Call it the Matthew effect with a passport: citations accrue to those who already have them, and here “those” tend to carry a US affiliation. The authors describe an American “citation ecosystem that reinforces its central position,” frequently at the expense of Global South scholarship, which stays on the periphery of both the publishing and the citing.

What this is, and what it is not

I want to be careful, because it’s easy to over-read one paper. This is a single field, and law may be the strangest field you could pick for citation counting. A large share of US legal scholarship lives in student-edited law reviews that behave nothing like a physics or a medical journal, many of them without clean metadata for the databases to even read. Law is also split by legal system, common law against continental, so a scholar’s work is often local by nature. Sasvári and Lendvai point out that researchers working in Islamic, customary, or Jewish law are “visibly non-existent” on the list, and that Scopus itself under-indexes non-English work. None of this is claimed to generalize. I wouldn’t assume the same US share turns up in molecular biology or applied mathematics.

The authors are also not telling you to burn the list. Their words: “Completely disregarding ranking such as this one is, of course, not necessary.” What they are saying is narrower, and more useful. In legal studies the list carries a loud geographic accent, so it should not be read as the measure of who the best legal scholars are.

So what do you do with the badge?

Use it for less than the certificate invites. If you’re on it, enjoy it. It’s a fair sign your work gets read and cited, which isn’t nothing. If you’re ranking other people with it, for hiring, promotion, or a tenure file, remember that in at least one field it rewards a postal code about as much as a mind. “This person made the top 2%” honestly means “this person publishes in well-cited venues.” That is worth something. It is not the same sentence as “this person is among the best scientists alive,” even though the yellow highlight invites you to read it that way.

And if the world’s top 2% is this sensitive to where you happen to publish, what is it we’re actually ranking?

Two numbers are worth a sticky note. In a study of 74,239 papers, the ones that had gone up as a bioRxiv preprint had, on average, 36% more citations and a 49% higher Altmetric Attention Score than the ones that had not. That is Fu and Hughey, “Releasing a preprint is associated with more attention and citations for the peer-reviewed article”, in eLife (2019), across 39 journals.

It is tempting to read that as a recipe: post the preprint, wait, collect the citations. That reading is wrong, and this whole post is about why. Posting a preprint is not the same as being read.

Do preprints really get more citations?

Yes, the association is real, and it is not one lonely dataset. On top of Fu and Hughey, Fraser and colleagues ran a separate large analysis in Quantitative Science Studies (2020) and also found a citation and attention advantage for bioRxiv preprints. In Fu and Hughey the effect held independent of subfield, number of authors, and even the journal’s access model and impact factor. So the number is not a fluke, and it is not just an artifact of where the paper eventually landed.

Does posting the preprint cause the extra citations?

This is where the sticky note gets a caveat written under it. Both studies are observational. Nobody ran the experiment where you take the same paper, flip a coin, and preprint one copy but not the other. So the 36% is a correlation, and correlations this size usually carry passengers.

The obvious passenger is self-selection. Authors do not preprint at random. People tend to post the work they are most confident about, the results they most want seen, the papers from labs that already have a mailing list and a following. The eLife peer reviews raise exactly this point: the advantage may reflect that preprinted papers are more interesting, or come from more visible groups, not that the act of posting did the lifting. Posting a weak paper does not summon citations.

Deposited, discoverable, and read are three different states

Here is the distinction the citation numbers quietly skip over. There are three things people collapse into one:

• Deposited. The file exists at a stable URL with a DOI. Congratulations, you and the server know it’s there.
• Discoverable. A stranger who isn’t searching your name can actually surface it, in Google Scholar, in a plain web search, in an AI answer.
• Read. A human, or the model answering that human, opens it, uses it, and cites or links it.

Depositing gets you the first one and none of the others for free. Most of the “preprint advantage” you actually want lives in the jump from deposited to discoverable. I wrote about the repository version of this in Deposit it and they won’t come: a study of 217,589 records where a few got almost all the search traffic and a long tail got essentially none. Uploading put every one of them in the repository. It did not put them in front of anyone.

What actually closes the gap?

Findability, and it is more boring and more doable than it sounds. Three things carry most of it.

Write the title and abstract as a quotable answer. Not a clever label, not reviewer-facing hedging. If a reader, or an AI search tool, asks the question your paper answers, the first sentence of your abstract should read like the answer, in the words a stranger would actually type. Answer engines lift sentences that already sound like answers.

Get the metadata and identifiers right. A registered DOI, correct author names and ORCIDs, the right field tags, a version that links cleanly to the published article once it appears. Broken or missing metadata is how a paper becomes invisible to the exact machines now doing the finding.

Build the link path. Discovery follows links. Point to the preprint from your profile pages, your lab site, and the posts where you talk about the work, so there’s an actual trail from where people already are to where the paper lives. A preprint with no inbound links is a house with no road to it.

Where Loud Camel fits

This is the part we work on. Loud Camel watches for places where your paper is the real answer to a question someone is asking right now, and drafts a response that points back to the preprint in plain language, with the link and the framing a searcher would use. You approve every draft. Nothing is sent on its own. The goal is narrow: shorten the distance from deposited to read, not to game a metric.

Posting the preprint is the floor. Whether anyone reads it is a separate job, and that job starts the moment the file is live, not the moment it gets cited. I could be wrong about how much findability moves the needle for any single paper. I’m fairly sure that leaving it to chance isn’t a plan.

Here is the joke. Garfield’s own paper on delayed recognition was itself delayed-recognized.

That’s the finding of a 2025 preprint by Tariq Ahmad Mir and Marcel Ausloos, Forsaking your own (arXiv:2512.16943). It is a preprint, so treat it as a strong claim, not a settled one. But the claim is clean, and I’ve been chewing on it for a week.

The numbers

Garfield published the paper in 1980. By 2004 it had collected about 10 citations. That is 0.4 citations a year for a quarter century, from the single most cited name in the field that invented citation counting. For 23 of those years it never once cracked more than one citation in a year. The authors call it a deep sleep of 28 years.

Then it woke. Counting up to 15 September 2025, they find 45 citations in Scopus, 93 in Web of Science, and 205 in Google Scholar, which they reconcile to 214 unique citing papers (234 if you count Garfield citing himself). A paper that averaged one citation every two and a half years for decades is now a normal, respectably cited paper. On the beauty coefficient, a standard sleeping-beauty score, it lands at 159.55 and 144.62 across two citation peaks, in 2018 and 2023. Whatever threshold you pick, it qualifies.

Twenty-three years of flat, then the wake-up. The main plot drops self-citations; the inset keeps them. Note where the floor ends: 2004, the year van Raan published.

Someone has to be the prince

What woke it up? Van Raan’s 2004 paper, the one that rechristened these papers “sleeping beauties.” After 2004 Garfield’s paper gets cited far more, and Mir and Ausloos note that in 141 of the 187 citations it collected after van Raan, roughly three quarters, it is co-cited with van Raan. The old paper rode in on the coattails of the new one.

There is an uncomfortable lesson here, and it is not about Garfield. Van Raan does not even cite Garfield’s 1980 paper. The revival happened anyway, because a well-placed, well-cited paper made the topic legible again and people went looking for the roots. Garfield’s paper needed a prince. It got one by luck, not because the work finally spoke for itself.

That’s the part I keep turning over. We tell ourselves that good work eventually gets found on merit. This is a case study in the opposite. The idea was correct. The author was famous. The paper still sat there for 28 years, and what saved it was somebody else’s visibility, not its own quality.

Where the story gets shakier

I run a company whose whole premise is that good research gets ignored and that being right isn’t enough. So I’m the last person you should trust to be skeptical here, and I want to be anyway.

“Delayed recognition” is not a fact of nature. It is a definition, and the definitions are somewhat arbitrary. Garfield’s own rule of thumb was 10 or fewer citations at age 10 and a tenfold jump by age 20. Van Raan added his own thresholds for the depth and length of the sleep. The beauty coefficient is yet another formula. Move the cutoffs and some sleeping beauties stop being beauties.

The authors are honest about a messier problem too. Garfield published in Current Contents, which was not peer reviewed, so Web of Science and Scopus record its metadata badly. Web of Science at one point piled the citations of 52 different articles from one 1980 issue onto a single paper. Every count above came from manual cleanup across three databases. And, again, it is a preprint. None of that kills the story. It just means the exact numbers matter less than the shape, and the shape is not in doubt.

If your work is being ignored right now

Read this as comfort or as a warning. I mean both.

The comfort: silence is not a verdict. Garfield’s paper was not wrong for 28 years and then suddenly right. It was the same paper the whole time. If your work is being ignored, that’s information about attention, not about quality.

The warning: attention does not show up on its own, and it did not show up for Garfield either. His paper waited a quarter century for an accident. Most papers never get a famous prince to co-cite them into daylight, and most of us don’t have Garfield’s name on the byline. If nobody knows the work exists, its merit never gets a turn.

So the question the paper leaves me with isn’t whether your good work will be recognized. It’s who, exactly, you’re counting on to be your prince, and whether it’s wise to wait for one to wander by.

Figure: Mir & Ausloos, Forsaking your own (arXiv:2512.16943), Figure 1. Reproduced under CC BY 4.0.

That burst has a name. Liu et al. called it a “hot streak” in 2018: a bounded stretch in a creative career during which a person produces their highest-impact work. The unsettling part of their result was that the streak seems to strike at a random time. You cannot schedule it. You get one, if you get one at all. I find this idea seductive, and I distrust things I find seductive, so a new paper caught my attention.

What the study found

Hongkan Chen, Lutz Bornmann, and Yi Bu asked a narrow, testable question. Not “when does your best work happen,” but “when your best work happens, is it also your boldest?” (Humanities and Social Sciences Communications, 2025) Their answer, from the careers of 21,271 economists: yes. “Disruptiveness of scientific publications is higher during researchers’ hot streaks than in their other periods of careers.” During the streak you’re not only cited more. You are also more likely to publish work that shoves the field in a new direction instead of reinforcing the old one.

What “disruptive” means here

That word needs a definition, because it is doing a lot of work. Some papers consolidate what we already know; later work leans on them and on their predecessors together. Other papers disrupt: later work cites the new paper and quietly stops citing what came before, as if the new paper made the old scaffolding unnecessary. The disruption index puts a number on that difference. A high score means your paper eclipsed its own references. The claim here is that hot-streak papers score higher on it.

Two findings I did not expect

First, the boldness is random too. Just as hot streaks land at unpredictable times, so does disruptive work: “we also observe the randomness rule for the occurrence of disruptive work.” You do not graduate into disruptiveness at a fixed career age.

Second, and this is the one I keep chewing on: “researchers’ most disruptive years are oftentimes earlier than their most highly cited years.” Your boldest paper and your most-cited paper are usually not the same paper, and the bold one tends to come first. The recognition lags the risk, sometimes by years.

What I don’t believe yet

I like this paper, and I don’t fully believe it yet.

It is economists. 21,271 of them is a large sample, but it is one field with its own citation culture. I wouldn’t bet the pattern transfers unchanged to molecular biology or to poetry.

The disruption index is contested. People argue about what it actually measures and how much it wobbles with database coverage and reference counts. Read “more disruptive” as “scored higher on one specific metric,” not as a verdict handed down by nature.

It is a correlation. Hot streaks and disruption travel together in this data. The paper doesn’t, and can’t, tell you that being disruptive triggers the streak, or that the streak makes you disruptive, or that some third thing drives both.

And one lever in the paper is not random at all: volume. “A higher publication volume of researchers increases the likelihood of entering hot streaks and producing disruptive publications.” Publish more, and you raise your odds of both. That is the least glamorous sentence in the paper and possibly the most useful.

Why care, if you are watching your own trajectory

If you’re staring at your own CV and wondering whether your good years are behind you or still ahead, this paper offers a small, cold comfort. The good years don’t seem to be something you earn on a schedule and then lose. They arrive when they arrive. What you can do is keep the tap open, because output feeds the odds. And if you’re sitting on a paper that feels riskier than your usual, the timing result hints that you might be earlier in a streak than the citation counts will admit for another few years.

The authors put the practical conclusion plainly, aimed at funders as much as scientists: “both scientists and funding agencies can assume the randomness of phases with important papers in scientific careers. But when these phases occur, impactful and disruptive papers can be expected.”

I’m glad they stopped there and didn’t try to sell me a recipe for manufacturing a hot streak. Because here’s what still nags at me. If the streak is random, and the disruption is random, and the two merely happen to move together, then the comforting story might collapse into something much plainer: sometimes a scientist has a good run, and during a good run good things cluster. Is that a law of creative careers, or is it just what any run of luck looks like from the inside? I’m not sure the data can tell those two apart. Are you?

A new preprint says the size part was mostly standing in for something else. Bili Zheng and Jianhua Hou, in “Synergy, not size”, argue that what drives disruptive work is not how many people sign a paper but how the collaboration is wired. It went up on arXiv in September 2025 and has not been peer reviewed, so read it as a strong claim, not a settled result.

What Zheng and Hou actually did

They looked at more than 14 million papers across 19 disciplines from 1960 to 2020. Instead of counting heads, they built what they call a synergy factor, a way to score the cost and benefit of adding people to a team. Then they ran a mediation analysis, which is a formal way of asking a simple question: when team composition predicts disruption, is size doing the work, or is something that size is merely correlated with doing the work? Their answer is that synergy, not team size alone, accounts for 75 percent of the link between who is on the team and how disruptive the paper turns out to be.

The best team size is not one number

The result that stuck with me is that there’s no single best size. Physics peaks at medium sized teams. The humanities reach their highest synergy through individual scholarship, one author alone. So “small teams disrupt” really means that the right architecture depends on the field, and in some fields the right architecture is a single person.

Star authors help, but not the ones you would rank highest

Two more numbers. Papers that include an exceptional researcher show, by their measure, 561 percent higher disruption. And a twist that should make anyone who reads CVs squirm: high-citation authors were linked to less disruptive potential, while authors with a track record of breakthroughs were linked to more. Being cited a lot and doing something new are not the same signal. Zheng and Hou sort teams into four modes: elite-driven, baseline, heterogeneity-driven, and low-cost.

So was “small teams disrupt” wrong?

Not really. This is not a refutation. It is a rewrite of the caption. Size was never the cause. It was a visible proxy for how a team combines skills and viewpoints. Small teams disrupt more, on average, because small teams are often wired for synergy by default. Build a large team that keeps that wiring and, in principle, you keep the disruption. That is a more useful claim than “stay small,” because wiring is something you can actually decide.

What I would not bank on yet

Three caveats, in order of how much they bother me. First, it is a preprint. No peer review, no independent replication that I’ve seen, and a 561 percent effect is exactly the kind of number a reviewer tugs on to see what falls off. Second, it is observational. Fourteen million papers is a mountain of correlation and zero experiments. Nobody randomly assigned scientists to teams. Third, and this is the one I keep circling back to, “disruption” here is a citation-pattern score, not a verdict on importance. The disruption index asks whether later work cites you instead of the things you built on. That captures something real, it also misses plenty, and the index has its own critics. When the paper calls a team disruptive, it means a shape in the citation graph, not a Nobel.

If you are choosing who to work with

Here is why a working researcher should care. If you’re picking co-authors, the size heuristic is easy and mostly wrong. This preprint’s version is harder and more honest: ask what each person adds that the others cannot, and whether adding them raises synergy or just raises the author count. A collaborator with one real breakthrough behind them may matter more than three with fat citation totals. And if you work in a field where solo work still disrupts, the reflex to bolt on co-authors may be quietly costing you the exact thing you were trying to buy.

I would hold all of it loosely until it clears review. But it does reframe a line I keep hearing repeated as if it were settled physics. Maybe the question was never how many people are in the room. Maybe it was always what happens once they are.

A new paper in Research Policy (Newig et al., 2026) says: not so fast. Jens Newig and thirteen co-authors reassess the whole framing, and their message is blunt. In the social sciences, a high disruption score usually does not mean a breakthrough. It measures something closer to noise: relabeled ideas, missing citations, subfields talking past each other. Progress there is cumulative anyway, so using disruptiveness to judge whether research matters, or to declare that science is stalling, is measuring the wrong thing. This is a conceptual paper, not a reanalysis. They ran no new data (the paper says so outright). What they did was take PLF apart argument by argument, drawing on the philosophy and sociology of science, and hand the field a set of testable hypotheses.

How does a paper score as “disruptive”?

The mechanism is simpler than it sounds, and that is the problem. The CD index looks at the papers that later cite yours, and asks whether they also cite the works you cited. If they keep citing your sources alongside you, you look consolidating. If they cite you and drop your references, you look disruptive, as though you rendered everything before you obsolete. Nothing in that arithmetic knows why the later citations skipped your references. Newig et al. list four kinds of papers that score as disruptive without disrupting anything:

• Pseudo-novelty. Old wine in new bottles: relabeling an existing idea with fresh terminology. Their own Scopus search turned up more than 4,900 papers with the phrase “fresh look” in the title, abstract or keywords, 27% of them in the social sciences and 21% in arts and humanities, even though those fields are only 8% and 4% of Scopus.
• Blockbuster and canonical papers. Citations that are ceremonial, name-dropping a famous work “to shine in their reflected glory” rather than depending on it.
• Citation gaps. Sloppy or strategic omission of prior work, which fakes the look of having displaced it.
• Purely cumulative papers. A meta-analysis or systematic review synthesizes a field so well that later authors cite only it and skip the originals. That is the textbook shape of cumulative science, and the CD index reads it as disruption.

Each type may hit only a subset of papers, but together, the authors argue, they add up to a meaningful share of artificially inflated scores.

Why the social sciences look the most disruptive

Here is the number that carries their case. In PLF’s own data, social science papers had the highest CD values of the four fields the entire time, falling from about 0.54 in 1945 to 0.04 in 2010, while life and physical sciences sat at the low end. Newig et al. read that ranking the opposite way to PLF. The social sciences do not look disruptive because they break more ground. They look disruptive because they are fragmented. Richard Whitley’s phrase for it is “fragmented adhocracy”: research that is “personal, idiosyncratic, and only weakly coordinated across research sites.” Watts (2017) puts it more bluntly, that in such fields “facts and theories pile up in an incoherent heap.” For a real Kuhnian disruption you first need a paradigm to disrupt. Where there is no shared consensus to overturn, what looks like disruption is what they call pseudo-disruption: an academic “turn,” a fashion, an outside influence, not a genuine break.

The trend was already shaky

And “disruption is declining” was contested before this paper landed. Independent reanalyses had pulled at it. Petersen and colleagues (2024) argue the decline is largely an artifact of citation inflation: reference lists have grown longer over the decades, which mechanically drags CD scores down. Others trace the patent version of the decline to the omission of older references in PLF’s own dataset. A separate line of critique (Leibel and Bornmann, 2024) notes that the index is sensitive to how many references a paper has and how well cited they are, and that social science papers, which cite books that citation databases like Web of Science do not index, get artificially inflated CD values because those book references are invisible to the machinery. So there were already technical reasons to doubt the trend.

What Newig et al. add sits one level up. Even computed perfectly, the index may not mean what evaluators want it to mean. A measure is only useful if it maps to the thing you care about. If the thing you care about is “did this work advance the field,” the CD index answers a different question and hands you a confident-looking number regardless. They push further than most of PLF’s critics: genuine, substantive disruption, the kind that truly renders earlier findings obsolete, may be rarer than the metric suggests, not more common.

Fragmentation, not disruption, is the real opposite of cumulation

They do not only poke holes. They offer a different map. Disruption and cumulation, they argue, are not two ends of one road. The real opposite of cumulation is fragmentation. So they draw two axes, disruptive-versus-consolidating and cumulative-versus-fragmented, and most good work lands in the cumulative-and-consolidating corner, which is just Kuhn’s “normal science”: replication, refinement, synthesis. A recent survey of 761 major breakthroughs (Krauss, 2024) found that virtually all of them developed cumulatively rather than by rupture, which fits the picture. Disruption earns its keep only when it does real work, mainly falsification: a failed replication that kills a wrong result, which feeds the cumulative pile rather than blowing it up. The classic case the authors borrow from PLF is Watson and Crick’s DNA model refuting Pauling’s triple helix. A new “turn” that merely changes the subject is not progress. It is, in their framing, fragmentation wearing novelty’s clothes.

Why this matters if a metric is scoring you

If you’re early or mid-career, this isn’t abstract. Disruption-style metrics are drifting into hiring talks, grant panels, and the dashboards that try to score a person. In a cumulative field, the paper that carefully extends three others is doing exactly what progress looks like, yet on a disruption index it scores low, while a disconnected, thinly-referenced outlier scores high. Optimize for the number and you would be nudged to cite less and to pretend your work sprang from nowhere. The authors say the quiet part directly: research policy and evaluators should not treat high disruptiveness as inherently valuable, or low disruptiveness as stagnation, and PLF’s declining-disruption conclusion “should be interpreted with care.” You do not have to win that argument in the room. You just have to be able to name it.

The limits are the authors’ own. They did not prove the four mechanisms dominate the data. They hypothesized them and invited the rest of us to test them empirically. Their case against PLF is an argument, a good one, not a verdict. And none of it proves science is fine, or that stagnation is a myth. Real slowdown might be happening. The narrower claim is that one popular number is a poor way to check, especially in the messy, unconsolidated fields where it happens to score highest. So before you let a disruption score speak for your work, or anyone’s, it’s worth asking the old question. Says who, and measuring what?

Sustained presence beats sporadic bursts. Two habits get you there, one written and one spoken. Neither asks for much per month. Both compound.

Answer one question a month where the conversation actually is

Your sub-sub-field already asks questions in public somewhere. A subreddit (r/MachineLearning, r/AskAcademia, a discipline subreddit), a Discord, a discipline mailing list, a journal-club Slack, a conference discussion channel, or a StackExchange site (CrossValidated for stats, MathOverflow, Physics SE, Academia SE). Find the one venue where people ask, not where they post.

StackExchange deserves special attention. Active question volume there has dropped since ChatGPT launched, and that cuts two ways in your favor. SE answers stay heavily indexed by Google and get ingested into LLM training sets at a far higher rate than tweets, so a good answer is one of the longest-lasting public artifacts you can make. And with fewer competing answers, a thoughtful new contribution is more likely to become the canonical one. Subreddits give you readers this week. SE gives you readers, and LLM citations, for years.

Here is the routine. Find the venue. Lurk for two weeks. Identify a recurring question whose answer is partly what your last paper was about. Write the answer. The opening paragraph has to answer the question completely, whether or not the reader ever clicks through to your paper. The link is the cherry, not the cake. Name an open question your paper doesn’t answer (honesty reads as competence), and mention your paper last.

Do that once a month and you produce twelve high-quality public artifacts a year. Each is visible to AI search engines. Each stays findable for years. Each is a small seed in the loop where visibility breeds more visibility.

Give the same talk many times instead of a new one each time

The same 30-minute talk, given four to six times a year to different audiences, beats a new talk every time. The audience changes; the message compounds.

Treating every talk as from-scratch is a huge tax. Pick one or two calling-card talks instead and give them often. The talk gets sharper. The slides get sharper. The message travels.

There is an ordering principle. When you’ve the choice, give the talk to the smallest, most-forgiving audience first and the highest-stakes audience last. Start at your own research-group meeting, where a forgiving room tells you what’s confusing. Polish. Department seminar a month later, where faculty tell you what’s under-developed. Polish. Then a peer institution’s seminar or a regional workshop. By the time it reaches a conference or an invited national talk, it’s genuinely sharp, and the audience that matters most sees the version you refined three or four times.

You will not always have the choice. Invitations arrive when they arrive. But when you do have the choice, optimize which audience sees the polished version.

When a new venue invites you, don’t rewrite. Adapt: a new abstract for that audience, a few bridge slides at the start, one slide to cut, one closing sentence connecting your work to a question that audience cares about. Thirty minutes of work instead of two weeks.

What to do this month

For the written habit:

• Identify the one venue where your sub-sub-field actually asks questions in public. Where they ask, not where they post.
• Lurk for two weeks.
• Pick a recurring question your work touches.
• Write an answer whose opening paragraph stands on its own.
• Schedule “answer one question this month” as a recurring monthly calendar item.

For the spoken habit:

• Pick the one paper or direction you most want to be known for over the next 12 months.
• Prepare a 30-minute talk: plain-language opening, one striking figure, one question you can answer with data, one you can’t yet.
• List six to eight venues in order of audience size and stakes. Give the talk in that order when you can.
• Email two organizers this week.
• When a new invite arrives, adapt rather than rewrite.

Start with the two emails and the calendar item. Everything else follows from those.

This post is part of the Loud Camel field guide to academic visibility. You can read the whole guide as a single PDF: the handbook.