Assessment in the post-psychometric era: Learning to love the subjective and collective
Bibliographic record
Abstract
Since the 1970s, assessment of competence in the health professions has been dominated by a discourse of psychometrics that emphasizes the conversion of human behaviors to numbers and prioritizes high-stakes, point-in-time sampling, and standardization. There are many advantages to this approach, including increased fairness to test takers; however, some limitations of overemphasis on this paradigm are evident. Further, two shifts are underway that have significant consequences for assessment. First, as clinical practice becomes more interprofessional and team-based, the locus of competence is shifting from individuals to teams. Second, expensive, high-stakes final examinations are not well suited for longitudinal assessment in workplaces. The result is a need to consider assessment methods that are subjective and collective.
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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 | 0.046 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.029 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".