Bibliographic record
Abstract
“An old tradition and a new technology have converged to make possible an unprecedented public good” (Budapest Open Access Initiative)1. Recent events are transforming the possibility of this unprecedented public good into a reality, with medical literature leading the way. The Directory of Open Access Journals lists close to 3,200 fully open access, peer-reviewed scholarly journals as of February 2008. More than 400 of the journals in DOAJ are in the health sciences. DOAJ is growing rapidly, adding more than 1.5 titles per calendar day. PubMedCentral (PMC) is the world’s largest open access archive, with well over a million items. An international network, PMC International, is envisioned, with copies of the whole archive around the world for preservation and security, as well as a local option for deposit. Watch for rapid growth of PMC as medical research funders, including the U.S. National Institutes of Health, Wellcome Trust, the U.K. Medical Research Council, and the Canadian Institutes of Health Research, among others, are requiring public or open access to the research they fund. There are implications, and leadership opportunities, for librarians in the open access environment.
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 | 0.014 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.229 | 0.098 |
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".