Eight ways to get a grip on validity as a social imperative
Bibliographic record
Abstract
Validity as a social imperative foregrounds the social consequences of assessment and highlights the importance of building quality into the assessment development and monitoring processes. Validity as a social imperative is informed by current assessment trends such as programmatic-, longitudinal-, and rater-based assessment, and is one of the conceptualizations of validity currently at play in the Health Professions Education (HPE) literature. This Black Ice is intended to help readers to get a grip on how to embed principles of validity as a social imperative in the development and quality monitoring of an assessment. This piece draws on a program of work investigating validity as a social imperative, key HPE literature, and data generated through stakeholder interviews. We describe eight ways to implement validation practices that align with validity as a social imperative.
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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 | 0.517 | 0.445 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.030 | 0.236 |
| Scholarly communication | 0.038 | 0.067 |
| Open science | 0.007 | 0.040 |
| Research integrity | 0.018 | 0.038 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".