Predicting teacher commitment: The impact of school climate and social–emotional learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The aim of this study was to investigate whether school climate and social–emotional learning impact teacher commitment. The sample included 664 public schoolteachers from British Columbia and Ontario in Canada. Participants completed an online questionnaire about teacher commitment, school climate, and social–emotional learning. Binary logistic regression analyses showed that positive school climates significantly predicted three forms of teacher commitment: greater general professional commitment, future professional commitment, and organizational commitment. Of the school climate variables, student relations and collaboration among staff predicted commitment. In addition, stronger beliefs and integration of social–emotional learning predicted two types of teacher commitment: greater general professional commitment and organizational commitment. Of the social–emotional learning variables, the support and promotion of a social–emotional learning culture across the school and comfort with and regular implementation of social–emotional learning in the classroom predicted greater teacher commitment. Implications for practice and research are discussed. © 2011 Wiley Periodicals, Inc.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it