Bibliographic record
Abstract
Cognitive diagnostic assessment (CDA) is designed to measure specific knowledge structures and processing skills in students so as to provide information about their cognitive strengths and weaknesses. CDA is still in its infancy, but its parentage is fairly well established. In 1989, two seminal chapters in Robert Linn's Educational Measurement signaled both the escalating interest in and the need for cognitive diagnostic assessment. Samuel Messick's chapter, “Validity”, and the late Richard Snow and David Lohman's chapter, “Implications of Cognitive Psychology for Educational Measurement”, helped solidify the courtship of cognitive psychology within educational measurement. The ideas expressed in these chapters attracted many young scholars to educational measurement and persuaded other, well-established scholars to consider the potential of a relatively innovative branch of psychology, namely, cognitive psychology, for informing test development. CDA can be traced to the ideas expressed in the previously mentioned chapters and, of course, to the many other authors whose ideas, in turn, inspired Messick, Snow, and Lohman (e.g., Cronbach, 1957; Cronbach & Meehl, 1955; Embretson, 1983; Loevinger, 1957; Pellegrino & Glaser, 1979). Since 1989, other influential articles, chapters, and books have been written specifically about CDA (see Frederiksen, Glaser, Lesgold, & Shafto, 1990). Most notably, the article by Paul Nichols (1994) titled “A Framework for Developing Cognitively Diagnostic Assessments” and the book coedited by Paul Nichols, Susan Chipman, and Robert Brennan (1995) appropriately titled Cognitively Diagnostic Assessment .
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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 | 0.022 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".