The Bookmark Procedure for Setting Cut-Scores and Finalizing Performance Standards: Strengths and Weaknesses
Why this work is in the frame
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Bibliographic record
Abstract
The Bookmark standard-setting procedure was developed to address the perceived problems with the most popular method for setting cut-scores: the Angoff procedure (Angoff, 1971). The purposes of this article are to review the Bookmark procedure and evaluate it in terms of Berk’s (1986) criteria for evaluating cut-score setting methods. The strengths and weaknesses of the Bookmark are critically examined and discussed. In general, the strengths of the Bookmark method are that it (a) accommodates constructed-response as well as selected-response test items; (b) efficiently accommodates multiple cut-scores and multiple test forms; and (c) reduces cognitive complexity for panelists. Despite unresolved issues like the choice and understanding of the response probability, the Bookmark method remains a promising procedure for setting cut-scores and finalizing performance standards.
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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 | 0.015 | 0.513 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it