Bibliographic record
Abstract
It is time for the UK to consider a national licensing process “T he intent to develop liberally educated graduates, rather than competent technicians, is what makes a university a university.”1 This statement stands to be challenged. Postgraduate medical training in the United Kingdom is undergoing profound change as the Modernising Medical Careers project introduces a generic competency based curriculum for all newly graduated (foundation) doctors. A new culture of assessment is developing which increasingly focuses on testing clinical skills in the workplace.2 Licensing processes will be quality assured by the Postgraduate Medical Education Training Board (PMETB). The principles formulated for this encourage reliable, well designed assessments mapped against the requirements of the General Medical Council's (GMC) Good Medical Practice guidance (www.gmc-uk.org/med_ed/default.htm), appropriate standard setting, lay involvement, and transparency of process for candidates (http://www.pmetb.org.uk/). Royal colleges are reviewing their accreditation processes to meet these requirements. Against this background it is time to reconsider undergraduate examinations for UK medical students. In contrast to the United States and Canada, where national licensing examinations are held, the …
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.028 | 0.038 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".