Ensuring That the Competent Are Truly Competent: An Overview of Common Methods and Procedures Used to Set Standards on High-Stakes Examinations
Bibliographic record
Abstract
Determining whether or not an examinee has met an adequate standard of performance constitutes a central task for licensure and certification bodies. Consequently, standard setting is a key activity for all certification and licensing testing programs. The purpose of this article is to provide an overview of methods that have been proposed for set a passing standard on an examination. First, the distinction between norm-referenced and criterion-referenced methods for setting a standard will be outlined. Then, both test-centered and examinee-centered methods for setting a passing standard will be explicated. The importance of factoring in the consequences of adopting a standard will also be illustrated via the Hofstee method. In the concluding section, important issues pertaining to the selection of panelists as well as the validation of the standard will be addressed briefly.
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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 | 0.126 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.027 | 0.014 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".