Bibliographic record
Abstract
mandates, openly accessible biomedical research results promise to become a substantial resource. It will be apparent from this review that most OA activity has so far occurred within the higher education research sector, leaving other sectors such as healthcare relatively unaffected. This situation appears to be changing, with signs that OA is being espoused to serve political agendas in health information, as seen in calls for clinical drug trial data to be made openly accessible, 3 and in the launch of Open Medicine , a new journal founded by staff who left the Canadian Medical Association Journal in an argument over editorial independence. 4 But for the NHS, perhaps the most significant development is the news that the Department of Health, already part of the eight-member consortium that funded UK PubMed Central, has introduced its own mandate 5 with effect from April 2007. This mandate requires deposit of papers supported by DH funding in a move that heralds a new era for dissemination of NHS research outputs and suggests official endorsement of Open Access as a principle to be embraced by the NHS more widely. Acknowledgement This paper is an updated version of a talk given at the CILIP HLG Annual
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.400 | 0.322 |
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".