Application of the Overclaiming Technique to Scholastic Assessment
Bibliographic record
Abstract
The overclaiming technique is a novel assessment procedure that uses signal detection analysis to generate indices of knowledge accuracy (OC-accuracy) and self-enhancement (OC-bias). The technique has previously shown robustness over varied knowledge domains as well as low reactivity across administration contexts. Here we compared the OC-accuracy index with multiple choice (MC) and short answer (SA) tests in assessing knowledge of introductory psychology topics in a sample of 108 undergraduates. Results indicated that OC-accuracy was (a) comparable to MC and SA in predicting overall course grades and (b) superior to SA tests in reliability achieved per unit administration time. By including the OC-bias index, the overclaiming method also adds a unique element to scholastic testing, namely, a measure of knowledge self-enhancement. The latter index was a negative predictor of overall course grade, suggesting a narcissistic self-destructiveness. Because the self-enhancement index adds no extra administration time to the knowledge measure, the overclaiming approach provides a more rich and efficient information source compared with traditional methods of scholastic assessment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".