“Not try to save them or ask them to breathe through their oppression”: Educator perceptions and the need for a human-centered, liberatory approach to social and emotional learning
Bibliographic record
Abstract
Introduction Social and emotional learning (SEL) has been identified as one approach to promote positive mental health outcomes while alleviating the stressors of systemic racism and a global pandemic. As the United States turns to SEL as a remedy for mental health challenges and the current civil unrest, it becomes increasingly relevant to understand what SEL means to those who use it the most to strengthen the implementation of current programs as well as to inform the development of new programs to fill existing gaps. Methods This abductive qualitative study expands prior research by exploring how in-service educators define SEL ( N = 427). Results Our findings highlight that educators perceive SEL as more expansive than current competency-based models. Educators describe SEL as a praxis that can be responsive to student and community needs, facilitate healing, and center humanity along with racial and social justice. Discussion We discuss implications that highlight the potential risks and harm that can be perpetuated by the current practice of SEL and, like the educators in our study, advocate for dismantling white supremacy structures in education through the co-creation of a humanizing SEL approach.
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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 | 0.031 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".