COLLABORATIVE ACTION RESEARCH TO IMPLEMENT SOCIAL-EMOTIONAL LEARNING IN A RURAL ELEMENTARY SCHOOL: HELPING STUDENTS BECOME “LITTLE KIDS WITH BIG WORDS”
Bibliographic record
Abstract
Research has shown that social and emotional learning (SEL) can benefit students in affective, interpersonal, communicative, and academic realms. However, teachers integrating SEL face a variety of logistical, pedagogical, and skill development challenges, including how to effectively facilitate classroom conversations on social justice and personal loss. This article draws from classroom observations, teacher conversations, interactive journals, and field notes to describe a seven-month-long university-school partnership to carry out an action research project in a high-poverty rural elementary school in the US. Teachers grappled with how to address race, immigration, and gender discrimination in a predominantly White community. Classroom vignettes, and teacher and author reflections, illustrate the iterative, developmental, and reciprocal aspects of learning between teachers and students, and between the university-based facilitator and teachers.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".