Bibliographic record
Abstract
Background: Preprint servers and alternative publication platforms enable authors to accelerate the dissemination of their research. In recent years there has been an exponential increase in the use of such servers and platforms in the biomedical sciences, although little is known about who, why and what experiences researchers have with publishing on such platforms. In this article we explore one of these alternative publication platforms, F1000 Research, which offers immediate publication followed by post-publication peer review. Methods: From an unselected cohort of articles published between 13 th July 2012 and 30 th November 2017 in F1000 Research , we provided a summary of who and what was published on this platform and calculated the percentage of published articles that had been indexed on a bibliographic database ( PubMed ) following successful post-publication peer review. We also surveyed corresponding authors to further understand the rationale and experiences of those that have published using this platform. Results: A total of 1865 articles had been published in the study cohort period, of which 80% (n=1488) had successfully undergone peer review and were indexed on PubMed within a minimum period of six months since first publication. Nearly three-quarters of articles passed the peer review process with their initial submission. Survey responses were received from 296 corresponding authors. Open access, open peer review and the speed of publication were the three main reasons why authors opted to publish with F1000 Research . Conclusions: Many who published with F1000 Research had a positive experience and indicated that they would publish again with this same platform in the future. Nevertheless, there remained some concerns about the peer review process and the quality of the articles that were published.
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 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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.109 | 0.182 |
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".