Teacher anger as a double-edged sword: Contrasting trait and emotional labor effects
Bibliographic record
Abstract
Abstract In contrast to teachers’ positive emotions, such as enjoyment and enthusiasm, teachers’ negative emotions and the regulation of negative emotions have received limited empirical attention. As the most commonly experienced negative emotion in teachers, anger has to date demonstrated mixed effects on teacher development. On the one hand, habitual experiences of anger (i.e., trait anger ) exhaust teachers’ cognitive resources and impair pedagogical effectiveness, leading to poor student engagement. On the other hand, strategically expressing, faking, or hiding anger in daily, dynamic interactions with students can help teachers achieve instructional goals, foster student concentration, and facilitate student engagement. The current study adopted an intensive daily diary design to investigate the double-edged effects of teachers’ anger. Multilevel structural equation modeling of data from 4,140 daily diary entries provided by 655 practicing Canadian teachers confirmed our hypotheses. Trait anger in teachers was found to impair teacher-perceived student engagement. Daily genuine expression of anger corresponded with greater teacher-perceived student engagement; daily faking anger impaired perceived student engagement, and daily hiding anger showed mixed results. Moreover, teachers tended to hide anger over time, and were reluctant to express anger, genuine or otherwise, in front of their students. Finally, genuine expression and hiding of anger had only a temporary positive association with teacher-perceived student engagement, with student rapport being optimal for promoting sustained observed student engagement.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".