Socially Responsive Design and Evaluation of a Workers’ Compensation Thesaurus for a Community Organization with Selective Application of Cognitive Work Analysis: A Case Study
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
This article presents a case study of the evaluation of social responsiveness and relevance of terminology used in a specialized thesaurus constructed for a community legal clinic library. The thesaurus is intended to assist in meeting information discovery and educational needs of a small organization that advocates on behalf of injured workers for legal and social justice within Ontario's workers’ compensation system. The authors include an overview of the thesaurus project and the historical context of workers’ compensation. They discuss the use of Cognitive Work Analysis as an evaluation methodology particularly appropriate to both the material and the clinic's culture of collaboration, with examples of its application in practice and some lessons learned.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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