Validation of the Van Overschelde et al. (2004) category norms: Results from five experiments
Bibliographic record
Abstract
Van Overschelde, Rawson, and Dunlosky (2004) collected category/exemplar norms from over 600 participants at three American universities. For each of 70 categories, participants generated as many exemplars as possible in 30 seconds. One measure computed from their data was the "TOTAL" statistic - the proportion of participants that generated a particular exemplar given a category. Five experiments in the present investigation tested the ability of the TOTAL statistic to predict Reaction Time (RT) in a category/exemplar verification task with 236 participants. The simple correlation between "TOTAL" and the natural log of RT was approximately -0.20 with the average cost (slope) from high TOTAL (near 1.0) to low TOTAL (0.02) on the order of 250 ms. The results are similar in magnitude to previous research using older Battig and Montague norms which the Van Overschelde et al., norms supersede suggesting that the updated norms are a suitable contemporary replacement.
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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 | 0.030 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".