We need to rethink the way we identify diamond open access journals in quantitative science studies
Bibliographic record
Abstract
Abstract With the announcement of several new diamond open access (OA) related initiatives and the creation of the Global Summit on Diamond Open Access, diamond OA is now at the forefront of the OA movement. However, while working on our recent Quantitative Science Studies publication and data sets, we noticed that temporarily waiving article processing charges (APCs) was a commonly used strategy by big publishers for some of their journals. In the absence of an index of diamond journals, most studies have operationalized the identification of diamond journals as a subset of gold journals that do not charge an APC. While this is a pragmatic approach, we fear that it could undermine the value of the research in understanding what we believe is more commonly understood by diamond OA. This letter discusses the need for bibliometric research to apply more nuance in how it operationalizes diamond OA beyond the absence of APCs. We call on the publishing sector to be more transparent in the costs of publishing. Ultimately, we argue that transparency and a long-term commitment to no-APC publishing are necessary for diamond OA to succeed, and that the research community needs to apply this standard when seeking to understand the model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.338 | 0.655 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.042 | 0.045 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".