Evaluating DETECT Classification Accuracy and Consistency When Data Display Complex Structure
Bibliographic record
Abstract
DETECT, the acronym for Dimensionality Evaluation To Enumerate Contributing Traits, is an innovative and relatively new nonparametric dimensionality assessment procedure used to identify mutually exclusive, dimensionally homogeneous clusters of items using a genetic algorithm ( Zhang & Stout, 1999 ). Because the clusters of items are mutually exclusive, this procedure is most useful when the data display approximate simple structure. In many testing situations, however, data display a complex multidimensional structure. The purpose of the current study was to evaluate DETECT item classification accuracy and consistency when the data display different degrees of complex structure using both simulated and real data. Three variables were manipulated in the simulation study: The percentage of items displaying complex structure (10%, 30%, and 50%), the correlation between dimensions (.00, .30, .60, .75, and .90), and the sample size (500, 1,000, and 1,500). The results from the simulation study reveal that DETECT can accurately and consistently cluster items according to their true underlying dimension when as many as 30% of the items display complex structure, if the correlation between dimensions is less than or equal to .75 and the sample size is at least 1,000 examinees. If 50% of the items display complex structure, then the correlation between dimensions should be less than or equal to .60 and the sample size be, at least, 1,000 examinees. When the correlation between dimensions is .90, DETECT does not work well with any complex dimensional structure or sample size. Implications for practice and directions for future research 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 | 0.074 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".