Making Diagnostic Inferences About Cognitive Attributes Using the Rule‐Space Model and Attribute Hierarchy Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this paper is to describe the logic and identify key assumptions associated with making cognitive inferences using two attribute‐based psychometric methods. The first method is Kikumi Tatsuoka's rule‐space model. This model provides a strong point of reference for studying the nature of diagnostic inferences because it is important in the evolution of skills diagnostic testing and it is well documented. The second method is a new procedure called the attribute hierarchy method that was developed from the rule‐space approach. Although the attribute hierarchy method shares many commonalities with rule space, it represents an extension by including an attribute hierarchy that serves as an explicit cognitive model of task performance designed to link psychometric practices with contemporary cognitive theories. In this paper, we describe and compare these two attribute‐based psychometric methods and identify new directions for research and practice in skills diagnostic testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| 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) | 0.000 | 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