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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.095 | 0.239 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".