The Multiple‐Use of Accountability Assessments: Implications for the Process of Validation
Bibliographic record
Abstract
Implications of the multiple‐use of accountability assessments for the process of validation are examined. Multiple‐use refers to the simultaneous use of results from a single administration of an assessment for its intended use and for one or more additional uses. A theoretical discussion of the issues for validation which emerge from multiple‐use is provided focusing on the increased stakes that result from multiple‐use and the need to consider the interactions that may take place between multiple‐uses. To further explore this practice, an empirical study of the multiple‐use of the Education Quality and Accountability Office Grade 9 Assessment of Mathematics, a mandatory assessment administered in Ontario, Canada, is presented. Drawing on data gathered in an in‐depth case study, practices associated with two of the multiple‐uses of this assessment are considered and evidence of ways these two uses interact is presented. Given these interactions, the limitations of an argument‐based approach to validation for this instance of multiple‐use are demonstrated. Some ways that the process of validation might better address the practice of multiple‐use are suggested and areas for further investigation of this frequently occurring practice are discussed.
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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 | 0.722 | 0.775 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.025 | 0.123 |
| Scholarly communication | 0.032 | 0.050 |
| Open science | 0.010 | 0.028 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".