Exploring the Structure of Teachers’ Emotional Labor in the Classroom: A Multitrait–Multimethod Analysis
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 This study tests whether teachers’ emotional labor in classroom settings is optimally conceptualized according to the type of emotional labor strategy involved (genuinely expressing, hiding, and faking emotions), the specific type of emotion being performed in class (e.g., enjoyment vs. pride vs. anxiety), or both strategy type and emotion type. Multitrait–multimethod analyses of 1,086 Canadian teachers’ survey responses showed teachers’ responses to emotional labor items to be most reliably differentiated according to both specific types of emotional labor strategies as well as the valence of the emotion being performed. Findings were largely consistent with common “display rules” encouraging expression of positive emotions and hiding of negative emotions by teachers in classroom settings. Results further showed teachers’ emotional labor strategies for negative emotions to be particularly contingent on the specific discrete emotion involved, highlighting the complexity of expressing negative emotions as a behavior management strategy.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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