Better together: An assessor support roadmap
Bibliographic record
Abstract
Assessors, in both clinical practice and academic settings, are pivotal in making judgements on learner performance to ensure members of the public are supported by graduates who are safe and competent practitioners. However, consistency of assessor judgements of learner performance has been a concern in directly observed clinical assessments such as workplace-based assessments (WBAs) and objective structured clinical examinations (OSCEs). A range of sociocultural factors could influence the consistency of assessor judgements such as assessors' beliefs about the purpose of an assessment, their perception of the usefulness of the marking criteria, their expectations of learner competence and their idiosyncratic judgement practices. These inconsistencies affect the high-stakes decisions made regarding learner progression or feedback provided that could impact their career development.
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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 | 0.187 | 0.178 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.025 | 0.034 |
| Open science | 0.011 | 0.052 |
| Research integrity | 0.019 | 0.025 |
| Insufficient payload (model declined to judge) | 0.039 | 0.025 |
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".