Bibliographic record
Abstract
s a student, I tolerated my occupational therapy the- ory class. I didn't fully appreciate the importance or value of it, and I didn't know how I was going to actu- ally use the concepts, definitions, and models that were being explained to me once I started to practice. It all seemed very abstract and rather esoteric. I wanted to learn how to do things and I didn't see how theory was going to help me do. At that point in my career, theory didn't matter. After read- ing the article by Hodgetts, Hollis, Triska, Dennis, Madill and Taylor (2007) in the June issue, it appears that my initial response to theory was not unusual. Students often express a desire to learn technical skills over theory and research. Fortunately, a lot has changed for me in 20 years. The importance of theory (finally) dawned on me in 1991 when I started to work for the Canadian Association of Occupational Therapist's Seniors' Health Promotion Project. My colleagues (Brenda Fraser, Lori Letts, and June Walls) and I set out to develop, demonstrate and evaluate the role of occupational therapists in health promotion with seniors. In a position where there were no role models and no clear parameters for what to do or how to do it, suddenly theory mattered - a lot. I remember re-reading my theory textbook and having the ah-ha moment. I realized how theory could help me decide what to do and what not to do during the course of the pro-
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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 | 0.077 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.063 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.019 | 0.034 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".