Reimagining social and emotional learning in Manitoba schools: developing a framework that supports a comprehensive policy approach
Bibliographic record
Abstract
This study examines the ways in which policy makers could improve support for social and emotional learning (SEL) practices in Manitoba schools. A lack of policy guidance around SEL in Manitoba has led to a pedagogical void that does not align with what literature suggests are best practices. To understand how this current approach might be improved, a sample of SEL frameworks from a variety of educational contexts were analysed using a three-stage content analysis method. The data resulting from this analysis was used to construct a new framework that represents a more comprehensive and explicit approach to SEL in Manitoban schools. This process resulted in a reimagined Manitoba policy framework with three new thematic areas (Critical and Creative Thinking, Self-Regulation and Self Awareness) as well as new and more descriptive sub-themes in all thematic categories, and the addition of a new layer of explicit criteria that supports better teaching and assessment practice. A discussion of why this framework represents an improvement from current policy and the implications for educators and policy makers are also provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".