The Bookmark Procedure for Setting Cut-Scores and Finalizing Performance Standards: Strengths and Weaknesses
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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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 | 0.125 | 0.335 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".