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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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 teacher head, 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".