Intensity Matters: The Interplay Between Teachers’ Trait Emotions and Emotional Labor in Predicting Burnout
Bibliographic record
Abstract
This pre-registration investigates the extent to which teachers’ trait emotions (joy, anger, anxiety, and shame) interact with corresponding emotional labor strategies to predict emotional exhaustion. Specifically, we examine whether hiding anger, anxiety, and shame and faking joy have stronger adverse effects when these emotions are experienced intensely and frequently, but weaker effects when emotions are less intense and frequent. Using a teacher sample from Germany and Canada, we assess each emotion and its associated regulation strategy through moderation analyses. We hypothesize that stronger intensity and frequency of emotions intensify the relationship between hiding and faking and emotional exhaustion. This research thus aims to clarify whether the interaction of emotional experiences and teachers’ emotional labor strategy jointly shapes teachers’ vulnerability to burnout.
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How this classification was reachedexpand
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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.013 | 0.009 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; both teacher heads agree on what is shown here.
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".