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 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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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; both teacher heads agree on what is shown here.
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".