Empirical lessons about occupational categorization from case studies of unemployment
Bibliographic record
Abstract
BACKGROUND: Scrutiny regarding the typological categorization of occupation (e.g., occupation as work, rest, or leisure) has prompted interest in experiential categories as a less exclusionary alternative. Empirical research can extend the dialogue about categorization by demonstrating how people in particular situations apply and generate occupational categories. PURPOSE: This article explores how adults without work utilized typological and experiential categorizations when discussing their occupations. METHOD: Data were generated via a secondary analysis of interview transcripts from three ethnographic case studies. FINDINGS: Study consultants gravitated toward experiential rather than typological categorizations, emphasizing the social, chosen, purposeful, and temporal qualities of their occupational engagement. IMPLICATIONS: Occupational therapy practitioners and researchers must explicitly state how and why they categorize occupations with clients and research participants. Whereas typological categories can be used to initiate discussions about occupation, open questions paired with consultant-generated experiential categories may better capture occupational engagement and reveal potential injustices in situations like unemployment
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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 | 0.040 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.018 | 0.029 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".