The early bird uses bioRxiv: The impact of career stage on the usage of preprints in ecology and evolution
Bibliographic record
Abstract
The usage of preprint servers in ecology and evolution is increasing, as it allows for research to be rapidly disseminated and available through open access at no cost. This is relevant for Early Career Researchers (ECRs), who must demonstrate research ability for funding opportunities, scholarships, grants, or faculty positions in short temporal windows in order to advance their careers. Concurrently, limited experience with the peer review process can make it challenging for those who are in the early stages of their research career to build publication records. Therefore, ECRs face different challenges relative to researchers with permanent positions and established research programs and have different requirements in terms of research output and timelines. These challenges might also vary according to institution size and country, which are associated with the availability of funding for open access journals. Herein, we hypothesize that career stage and institution size impact relative usage of preprint servers among researchers in ecology and evolution. Using data collected from 500 articles (100 from each of two open access journals, two closed access journals, and a preprint server), we demonstrate that ECRs generate more preprints relative to non-ECRs, for both first and last authors. We speculate that this pattern is reflective of the advantages of quick and open access research that is disproportionately beneficial to ECRs. There is also a marginal effect of first author institution size on preprint usage, whereby the number of preprints tends to increase with institution size for ECRs, although the interaction between ECR status and institution size was not significant. The United States and United Kingdom contributed the greatest number of preprints by early career researchers, whereas non-western countries contributed relatively fewer preprints. This research provides empirical evidence regarding motivations of preprint usage and barriers surrounding large-scale adoption of preprinting in ecology and evolution.
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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 | 0.041 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".