When Teachers have Autonomy to create SEL Initiatives: Conceptualizations and Iterations
Bibliographic record
Abstract
Teachers may be encouraged to follow a prescribed curriculum when teaching social and emotional learning (SEL), and varied research findings attest to the efficacy of this approach in fostering students’ social and emotional competencies. An alternative approach might see teachers create SEL initiatives and infuse, embed, or integrate SEL into core teaching content. This case study explored how, when asked to foster social and emotional learning within their schools, 16 SEL teachers created learning opportunities for students to bolster their social and emotional skills. Teachers were asked to first define SEL and then to create portfolios showcasing three of their SEL lessons or initiatives. Content analysis of definitions revealed that teachers largely defined SEL as fostering students’ self-awareness and self-management. Content analysis of each of the teachers’ lessons indicated that the learning opportunities or initiatives that teachers introduced were predominantly social in nature and oftentimes focused on having students practice emotion regulation strategies. Findings inform our understanding of the perceptions and actualizations of SEL in applied contexts. Keywords: social emotional learning, elementary schools, teachers, case study, content analysis
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".