Bibliographic record
Abstract
A study published today in PLoS Biology provides robust evidence that open-access articles are more immediately recognized and cited than non-OA articles. This editorial provides some additional follow up data from the most recent analysis of the same cohort in April 2006, 17 to 21 months after publication. These data suggest that the citation gap between open access and non-open access papers continues to widen. I conclude with the observation that the "open access advantage" has at least three components: (1) a citation count advantage (as a metric for knowledge uptake within the scientific community), (2) an end user uptake advantage, and (3) a cross-discipline fertilization advantage. More research is needed, and JMIR is inviting research on all aspects of open access. As the advantages for publishing open access from a researchers' point of view become increasingly clear, questions around the sustainability of open access journals remain. This journal is a living example that "lean publishing" models can create successful open access journals. Open source tools which have been developed by the Public Knowledge Project at the University of British Columbia with contributions from the Epublishing & Open Access group at the Centre for Global eHealth Innovation in Toronto are an alternative to hosting journals on commercial open access publisher sites.
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 | 0.017 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".