Bibliographic record
Abstract
‘But where are the people concerned in our training?’ This question is central for the School of Social Work at the University of Sherbrooke (Québec, Canada) to train students to think critically, to show openness and solidarity to diversity, and to act with competency. Consequently, the academic programme is structured to facilitate the involvement of service users in programme design and delivery. Gap mending and the intersection of knowledge are two principles that shape the inclusion of health and social service users in training activities. In this perspective, we promote the meeting of different actors that carry scientific, professional and experiential knowledge. In the end, this generates mutual influence between actors. It provides a space for each individual&s;s voice to be heard with dignity. These measures echo the values of social work practice. Accordingly, this chapter proposes an exploration of the innovative aspects of this pedagogical approach exemplified by three projects: the use of a web tool (Baromètre) to facilitate the meeting of users and students in a practical activity, a summer school that gathers users and students for five intensive days and a four-day simulation that involves the knowledge of an Indigenous person, and involves the students in an advocacy situation. We discuss the nature of these activities, their challenges, and the added value for users, students, and professors.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".