The promise of community-driven preprints in ecology and evolution
Bibliographic record
Abstract
Publishing preprints is quickly becoming commonplace in ecology and evolutionary biology. Preprints can facilitate the rapid sharing of scientific knowledge establishing precedence and enabling feedback from the research community before peer review. Yet, significant barriers to preprint use exist including language barriers, a lack of understanding about the benefits of preprints and a lack of diversity in the types of research outputs accepted (e.g., reports). Community driven preprint initiatives can allow a research community to come together to break down these barriers to improve equity and coverage of global knowledge. Here, we explore the first preprints uploaded to EcoEvoRxiv (n = 1216), a community-driven preprint server for ecologists and evolutionary biologists, to characterise preprint practices in ecology, evolution and conservation. Our perspective piece highlights some of the unique initiatives that EcoEvoRxiv has taken to break down barriers to scientific publishing by exploring the composition of articles, how gender and career stage influence preprint use, whether preprints are associated with greater open science practices (e.g., code and data sharing), and tracking preprint publication outcomes. Our analysis identifies areas that we still need to improve upon but highlight how community-driven initiatives, such as EcoEvoRxiv, can play a crucial role in shaping publishing practices in biology.
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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 | 0.126 | 0.399 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 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".