Turning words into numbers: establishing an empirical cut score for a letter graded examination
Bibliographic record
Abstract
BACKGROUND: High stakes postgraduate specialist certification examinations have considerable implications for candidates' future careers. The cut score i.e. pass/fail mark of such examinations needs to be determined in a defensible and credible manner. A number of methods, suitable for use with numeric scoring methods, have been described. Determining the cut score of letter-graded examinations is, however, not described in the literature. AIM: The aim of this study was to determine a defensible and credible method for deriving the cut score of a letter-graded examination. METHOD: The cut score of the Fellowship examination of the College of Physicians of South Africa was estimated using a novel method. This method was validated by comparing the results obtained to those obtained using the contrasting groups method. RESULTS: By using the examiners' decision as the 'gold standard' we found that a cut score of 50% best approximated the cutpoint of this letter-graded examination, achieving a sensitivity and specificity of 83.7% and 82.8% respectively. CONCLUSION: This paper describes a useful strategy for estimating the cut score of letter-graded examinations.
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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 | 0.025 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".