Using the Attribute Hierarchy Method to Identify and Interpret Cognitive Skills that Produce Group Differences
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Bibliographic record
Abstract
The purpose of this study is to describe how the attribute hierarchy method (AHM) can be used to evaluate differential group performance at the cognitive attribute level. The AHM is a psychometric method for classifying examinees' test item responses into a set of attribute‐mastery patterns associated with different components in a cognitive model of task performance. Attribute probabilities, computed using a neural network, can be estimated on each attribute for each examinee thereby providing specific information about the examinee's attribute‐mastery level. These probabilities can also be compared across groups. We describe a four‐step procedure for estimating and interpreting group differences using the AHM. We also provide an example using student response data from a sample of algebra items on the SAT to illustrate our pattern recognition approach for studying group differences .
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it