Standard-setting methodology: Establishing performance standards and setting cut-scores to assist score interpretation
Bibliographic record
Abstract
A critical step in the development and use of tests of physical fitness for employment purposes (e.g., fitness for duty) is to establish 1 or more cut points, dividing the test score range into 2 or more ordered categories reflecting, for example, fail/pass decisions. Over the last 3 decades elaborated theories and methods have evolved focusing on the process of establishing 1 or more cut-scores on a test. This elaborated process is widely referred to as "standard-setting". As such, the validity of the test score interpretation hinges on the standard-setting, which embodies the purpose and rules according to which the test results are interpreted. The purpose of this paper is to provide an overview of standard-setting methodology. The essential features, key definitions and concepts, and various novel methods of informing standard-setting will be described. The focus is on foundational issues with an eye toward informing best practices with new methodology. Throughout, a case is made that in terms of best practices, establishing a test standard involves, in good part, setting a cut-score and can be conceptualized as evidence/data-based policy making that is essentially tied to test validity and an evidential trail.
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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 | 0.297 | 0.377 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".