Are we paying-to-play? A quantitative assessment of Canadian open access research in ecology and evolution
Bibliographic record
Abstract
Open access (OA) allows for peer-reviewed research to be freely accessed and there has been a collective shift from both researchers and publishers towards more OA publishing. OA typically occurs either through article-processing charges (the gold road) or via self-archiving (the green road); the former can be expensive, while the latter has seen minimal uptake. The gold road of OA has led to predatory publishers and, to some, questionable publications. Here, I used publicly available grant information in Canada and combined this with individual publishing statistics to test a variety of factors and their influence on OA publishing. I showed that an individual’s award amount, H-index, and gender did not influence the proportion of OA articles they published, but an individual’s H-index scaled with the number of OA publications. Institute size influenced OA publishing patterns, with researchers at large universities (i.e., >20 000 full-time students) publishing proportionately more OA articles than medium and small institutes. I discuss the potential for this pattern to build on pre-existing systemic biases when it comes to funding and publishing.
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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 | 0.027 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.037 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".