A proposed framework for conducting data-based test analysis.
Bibliographic record
Abstract
The authors argue that the current state of applied data-based test analytic practice is unstructured and unmethodical due in large part to the fact that there is no clearly specified, widely accepted test analytic framework for judging the performances of particular tests in particular contexts. Drawing from the extant test theory literature, they propose a rationale that may be used in data-based test analysis. The components of the proposed test analytic framework are outlined in detail, as are examples of the framework as applied to commonly encountered test evaluative scenarios. A number of potential extensions of the framework 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 | 0.117 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| 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".