Bibliographic record
Abstract
The five-parameter strategy developed by Edwards (1994) and Edwards and Cable (2009) has been widely used in congruence research for testing the perfect fit effect. This classical testing strategy, however, has a vulnerability: it includes the curvature of the surface along the misfit line (a4) and the intercept of the first principal axis (p10) but excludes the slope of the surface along the misfit line (a3). We argue that other things being equal, the combination of a4 and p10 is less reliable for testing the variation of the outcome along the misfit line, compared to the combination of a4 and a3. Due to this limitation, this popular five-parameter strategy, when used to close the empirical loop for congruence research, may lead to misleading conclusions, rather than accurate inferences and precise practical implications. To improve this situation, we propose to refine this classical testing strategy. Specifically, the new testing strategy we propose replaces p10 with a3, while retaining the other four parameters suggested by Edwards and Cable (2009). This one small change to the classical testing strategy allows a giant leap for congruence research, promising more robust theory development and scientific rigor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".