Reframing Teacher Engagement: A Framework for Improving Workplace Conditions to Foster Teacher Engagement
Bibliographic record
Abstract
Historically, the factors contributing to teacher engagement have been premised on the idea that engagement is grounded in intrinsic motivation and intrapersonal characteristics. This narrative literature review offers an alternative perspective, whereby teacher engagement is enhanced through creating policies to improve workplace conditions and foster engagement. Within the findings, three essential conditions to foster teacher engagement were identified: a supportive organization, active leadership, and healthy interpersonal relationships. A conceptual framework based on these findings is constructed and proposed as an aid to school leaders in policy development to improve teacher engagement. Keywords: Teacher engagement, conditions, policy, framework, school leaders Historiquement, les facteurs contribuant à l'engagement des enseignants ont été basés sur l'idée que l'engagement est fondé sur la motivation intrinsèque et les caractéristiques intrapersonnelles. Cette analyse documentaire narrative propose une autre perspective, selon laquelle l'engagement des enseignants est renforcé par la création de politiques visant à améliorer les conditions de travail et à favoriser l'engagement. Dans les conclusions, trois conditions essentielles pour favoriser l'engagement des enseignants ont été identifiées : une organisation de soutien, un leadership actif et des relations interpersonnelles saines. On construit un cadre conceptuel basé sur ces résultats et on le propose au leadership scolaire comme appui au développement de politiques visant à améliorer l'engagement des enseignants. Mots clés : Engagement des enseignants, conditions, politique, cadre, leadeurship scolaire
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".