Bibliographic record
Abstract
How much does it matter if students sitting clinical examinations, or being interviewed for medical school, are told about the content of the assessment by fellow students? Concern about “breaches of security” leads assessors to go to extreme lengths, such as keeping groups of candidates apart or frequently changing the content of the assessment. This may not be necessary. Three studies conducted at McMaster University, Canada, looked at the effect of defined violations of test security in a multiple mini-interview process on the outcome of student admissions. The first “leak” by a student about one of the tests was posted on a website within seven minutes of completing the test. Nevertheless, the authors found no differences in performance between students who had been informed by peers and those who had not. Medical Education 2006;40: 36–42 [OpenUrl][1][PubMed][2] A medical student addresses the thorny problem of the cynicism that affects students as they progress through their studies. This … [1]: {openurl}?query=rft.jtitle%253DMedical%2Beducation%26rft.stitle%253DMed%2BEduc%26rft.aulast%253DReiter%26rft.auinit1%253DH.%2BI.%26rft.volume%253D40%26rft.issue%253D1%26rft.spage%253D36%26rft.epage%253D42%26rft.atitle%253DThe%2Beffect%2Bof%2Bdefined%2Bviolations%2Bof%2Btest%2Bsecurity%2Bon%2Badmissions%2Boutcomes%2Busing%2Bmultiple%2Bmini-interviews.%26rft_id%253Dinfo%253Apmid%252F16441321%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=16441321&link_type=MED&atom=%2Fbmj%2F332%2F7547%2F972.atom
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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.046 | 0.028 |
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".