Establishing systemic social and emotional learning approaches in schools: a framework for schoolwide implementation
Bibliographic record
Abstract
Social and emotional learning (SEL) is a fundamental part of education. Incorporating high-quality SEL programming into day-to-day classroom and school practices has emerged as a main goal for many practitioners over the past decade. The present article overviews the current state of SEL research and practice, with a particular focus on the United States. The need for a model of SEL that goes beyond the classroom is illustrated, and a systemic approach to implementing SEL school-wide is introduced. It is argued that school-wide SEL maximises the benefits of SEL programming by becoming the organising framework for fostering students’ potential as scholars, community members, and citizens. Further, a Theory of Action (ToA) developed by the Collaborative for Academic, Social, and Emotional Learning (CASEL) is presented that serves as a blueprint for implementing systemic SEL in schools. Potential challenges and barriers involved in moving toward school-wide SEL implementation are considered and discussed.
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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 | 0.062 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.015 | 0.078 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".