What’s for Lunch? A Helping Professions Faculty Collaborative That
Bibliographic record
Abstract
It has opened a broader view of possibilities for collaboration with other colleagues in other professions than I could have imagined. Highly encour-aging was the recognition that other departments were as desirous as our own of initiating this kind of dialogue. (Faculty 7) This quote from a participant in the project describe here highlights changes in the health and education sectors during the last quarter century in which service provision has moved from discipline specific strategies to a more collaborative, transdisciplinary, team approach to improve care options for those in need (Friend & Cook, 2000; Cramer, 1998; Sadao, 2001). Influencing this trend has been the change from service-centered philosophies to person-focused and family-centered efforts evident in the helping professions (Turnbull & Turnbull, 1997). Helping professions have shifted paradigms in which they operate from What’s for Lunch?52 Issues in Teacher Education
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.039 | 0.021 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".