The promise of community-driven preprints in ecology and evolution
Bibliographic record
Abstract
Here, we explore the first preprints uploaded to EcoEvoRxiv to characterise preprint practices in ecology and evolution. We aim to understand: 1) in what countries authors who use EcoEvoRxiv are located; 2) the taxonomic diversity of study systems across preprints; 3) whether preprint server use depends on career stage and gender; 4) the extent to which authors make use of preprint servers for reports and community-driven peer review; 5) the extent to which data and code are shared in preprints; and 6) how many preprints remain unpublished, and for those that are published, how long it took for them to become published. In the process, we also provide a summary of what makes EcoEvoRxiv distinct from other preprint servers to help further clarify the benefits of using community-driven preprint servers to disseminate research findings.
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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 | 0.140 | 0.347 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.027 | 0.027 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".