Four ways to get a grip on making robust decisions from workplace-based assessments
Bibliographic record
Abstract
Synthesising the results of workplace-based assessments to inform robust decisions is seen as both important and difficult. Concerns about failing to fail the trainee not ready to proceed has drawn disproportionate attention to assessors. This paper proposes a model for a more systems-based view so that the value of the assessor's judgement is incorporated while preserving the value and robustness of collective decision-making. Our experience has shown it can facilitate robust decisions on some of the more difficult areas, such as professionalism.
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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 | 0.229 | 0.272 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.007 | 0.056 |
| Scholarly communication | 0.041 | 0.052 |
| Open science | 0.009 | 0.027 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".