CHAPTER 11 Teaching Active Offer: Proposal for an Educational Framework for Professors
Bibliographic record
Abstract
N ot only do we need to train health care and social service pro- fessionals about active offer, we must also train their trainers (professors).Most professors teaching in health care and social service programs in French have not received training in teaching strategies to prepare future professionals who will one day work in minority Francophone communities.This prompted us to examine the most appropriate type of education for these professors.This chapter explores educational perspectives on andragogy and presents our conceptual framework for education, including the pedagogical setting and types of knowledge needed (content knowledge, skills [know-how], attitudes [soft skills], as well as how to put those into action [knowledge to act]), to prepare professionals to work in the area of active offer.Finally, we offer our thoughts on the particular issues and challenges of teaching active offer, as identified in a pilot project to implement education on active offer.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".