Overview
A recent post on the Open Café listserv posed a question about the true extent of fee-free open access publishing, but it noted the incomplete coverage of the data cited. We have more comprehensive data, but just as we started our analysis, DeepSeek’s release sent markets into turmoil. The stage was set for a timely experiment. We first answer the question using our data. Then we see how the AI did.
Background
What proportion of open access is not paid for by APCs? In discussing this, a recent Open Café listserv post cited studies by Walt Crawford – a librarian, well-known in the academic library and OA communities for his analysis of open access. He has paid particular attention to “diamond” OA journals, which charge neither readers nor authors. His studies are based on data from the Directory of Open Access journals (DOAJ). Excellent though both sources may be – and, full disclosure, we contribute to the DOAJ – the DOAJ’s remit covers only fully OA (“gold”) journals.
As listserv founder Rick Anderson noted, “By counting only articles published in DOAJ-listed journals, Crawford’s studies radically _undercount_ the number of APC-funded OA articles published – because DOAJ does not list hybrid journals, which always charge an APC for OA and which produce a lot of genuinely OA articles (though exactly how many, no one knows).”
Using our data
Actually, we do know … or at least have some fair estimates of hybrid OA. Our data allows us to determine the share of open access output in APC-free journals, as follows.
Sources: Publishers' websites, OpenAlex, DOAJ, Delta Think analysis. © 2025 Delta Think Inc. All rights reserved.
The charts analyze shares of open access articles published in both fully OA and hybrid journals.
Our estimate of a 13% APC-free share of the total market compares with around 33% noted in the Open Café post, citing Walt Crawford’s data.
What about AI?
If we wanted to use public sources, we would consider this to be a classic web research problem. This should suit generative AI (GenAI). ChatGPT is arguably the most well-known example of this technology. With disruptor DeepSeek grabbing headlines recently, it seemed opportune to compare both with the benchmarks from our own data. We can also test DeepSeek’s claims to deliver an equivalent level of performance to ChatGPT.
The actual question
Users of GenAI tools rapidly discover that the phraseology and nuances of questions asked – aka the prompt – are vital. The more detail and direction you give them, the better. We used:
what proportion of open access is diamond open access? list numbers of articles
and share for each of the last 5 years
Some prompts can run to paragraphs. But we wanted to test the AI’s ability to process a natural sounding and simple question, to interpret the term Diamond, and to see if it would spot the inclusion of fully OA and hybrid. We needed to direct it to the specifics of our required results. The 5-year period was mentioned in the Open Café post. We chose a “one shot” prompt (not asking the model to refine its first answer), as we wanted to see how our virtual research assistant would perform without further direction.
The answer
The table below shows the results.
*ChatGPT 4o did not give the answer directly. Instead, it gave Diamond OA & APC-funded OA as separate shares of all output, so we needed to calculate Diamond share of OA.
†ChatGPT 4-turbo with “Reasoning” switched on.
The table above summarizes the AIs’ answers.
A research assistant’s ability to explain their results is important, and so we assessed the models on their communication ability. In practice both research assistants and AI can be asked to refine their work. But for the purposes of this analysis, we used a one-shot prompt.
Conclusion
In working with and assessing GenAI models, it is important to set expectations correctly. They are more like an inexperienced intern than a seasoned researcher. Prompts need to be refined, several attempts made, and human judgement exercised over the results.
DeepSeek’s results were surprisingly good. It undershot our estimate by a few percentage points. ChatGPT overestimated considerably – perhaps reflecting its use of the problematic sources noted in the original listserv post. The additional material selected by the models was useful in explaining the concepts and drivers behind that data. The differences between our samples and those the models cite explain some of the variance in the data. But this doesn’t explain some of the big differences – e.g. same #s articles, but different shares or vice versa, with 2.5x variance.
Perhaps the GenAI models, in their current forms, are not ideally suited this sort of numerically focused research. However, they appear to be progressing in the right direction. (Note: We did not attempt this query on either of the Deep Research models, currently offered by OpenAI and Google Gemini.)
For fun, we tail-ended our assessment by asking each of our AIs “what's the answer to life, the universe and everything?” ChatGPT went to the Hitchhiker’s Guide to the Galaxy answer of 42. DeepSeek was more expansive, noting how the question encompassed broader philosophic endeavors. However, a light-weight offline version of DeepSeek that we experimented with gave perhaps the best reply. It concluded that, while 42 is a clever punchline,
“…it also reflects on the human tendency to search for significance where there might not be one.”
Fair point.
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This article is © 2025 Delta Think, Inc. It is published under a Creative Commons Attribution-NonCommercial 4.0 International License. Please do get in touch if you want to use it in other contexts – we’re usually pretty accommodating.
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