Setting Cut-Scores for Complex Performance Assessments: A Critical Examination of the Analytic Judgment Method
Bibliographic record
Abstract
The purpose of this article is to promote an increased awareness of the processes for setting cut-scores for complex performance assessments by (a) describing the Analytic Judgment Method (AJM) for setting cut-scores, and (b) critically evaluating the technical adequacy and practicability of the AJM by focusing on one investigation where the AJM was used by Plake and Hambleton (2001) for setting standards on the Pennsylvania Grade 8 Mathematics Achievement Test. Although Plake and Hambleton (1998, 2001) demonstrate that the AJM is an attractive iterative procedure that uses independent judgments of actual student work, more research is necessary to replicate the results and determine whether the AJM would produce high interrater reliability with more traditionally sized panels of 20 or more representatives.
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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 | 0.618 | 0.802 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.006 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.001 | 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".