“Exhaustive but effective”: A multi-site study investigating the profiles of teachers' emotions and emotional labor
Bibliographic record
Abstract
Teachers routinely experience and manage a variety of emotions to meet the requirements of their profession. Previous research has primarily focused on how teachers' emotions or their emotional labor affects their well-being and teaching quality. The present study takes a more holistic, person-centered approach to identify groups of teachers with distinct emotional experiences and emotional labor tendencies. In the first study, 474 Canadian secondary school teachers (female: 72.5%) were categorized into three profiles: emotionally healthy deep actors, emotionally healthy surface actors, and emotionally unhealthy surface actors. The emotionally healthy deep actors reported the highest levels of well-being, while the emotionally unhealthy surface actors reported the lowest. The same profiles were observed in Study 2 with 85 German secondary school teachers (female: 57.6%). Among these teachers, the emotionally healthy surface actors were rated by students as the most supportive ( N students = 1327). Conversely, the emotionally unhealthy surface actors received the least favorable student ratings of teaching quality (cognitive activation, classroom management, student support). In conclusion, our study indicates that emotional labor, specifically surface acting, has a double-edged function, with both positive and negative implications. On the one hand, it is linked to diminished well-being among teachers, while on the other hand, it has the potential to enhance students' perceptions of teacher support.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".