Technical and social issues influencing the adoption of preprints in the life sciences
Bibliographic record
Abstract
Preprints are gaining visibility in many fields.Thanks to the exponential growth in submissions to bioRxiv, an online server for preprints in biology, versions of manuscripts prior to the completion of journal-organized peer review are poised to become a standard component of the publishing experience in the life sciences.Here, we provide an overview of current challenges facing preprints, both technical and social, and a vision for their future development. Unbundling the functions of publicationScience progresses only at the rate at which we can share information with one another.But as any author of a journal article can attest, formal mechanisms of scholarly communication do not always work efficiently and can be subject to biases [1-3].Peer review takes time: not merely for the reviewer to compile a thorough assessment but also for the editor to find reviewers.In the swiftest case, a manuscript is accepted at the first journal, and the process to eventual publication may take approximately four months [4,5].However, given that many researchers continue to be evaluated based on the reputation of the journals in which their work is published, authors are incentivised to "aim high" when they select a journal, and it can take several rounds of review (at a single or multiple journals) before the work is approved for publication.It is commonplace for a manuscript to have been submitted to at least two journals on its way to publication, and as a result, the overall peer review process can take years [6].The sooner a piece of work can be read, evaluated, and built upon, the faster science moves.And by including a greater diversity of thought in the process of science, higher quality final products emerge.Yet, although our system of publication has superficially transitioned from physical print magazines to online websites, the mechanisms and processes of scientific communication are not much faster or more inclusive than they were in the 19th century.Perhaps the underlying cause for this stasis is the fact that our system of evaluating scientific work-whether for deciding what to read or to whom to award grants and jobs-relies heavily on the reputation of journal titles, and in turn, journal article output is factored into university ranking calculations [7].Experimenting with new forms of sharing science that are incompatible with publication in traditional venues therefore carries career risks.In addition, many open-science practices (posting lab notebooks, sharing data sets, or conducting replication
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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 | 0.057 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".