The Glass Half Empty: How Emotional Exhaustion Affects the State-Trait Discrepancy in Self-Reports of Teaching Emotions
Bibliographic record
Abstract
Following from previous research on intensity bias and the accessibility model of emotional self-report, the present study examined the role of emotional exhaustion in explaining the discrepancy in teachers' reports of their trait (habitual) versus state (momentary, "real") emotions. Trait reports (habitual emotions, exhaustion) were assessed via trait questionnaires, and state reports (momentary emotions) were assessed in real time via the experience sampling method by using personal digital assistants (N = 69 high school teachers; 1,089 measures within teachers). In line with our assumptions, multi-level analyses showed that, as compared to the state assessment, teachers reported higher levels of habitual teaching-related emotions of anger, anxiety, shame, boredom, enjoyment, and pride. Additionally, the state-trait discrepancy in self-reports of negative emotions was accounted for by teachers' emotional exhaustion, with high exhaustion levels corresponding with a greater state-trait discrepancy. Exhaustion levels did not moderate the state-trait discrepancy in positive emotions indicating that perceived emotional exhaustion may reflect identity-related cognitions specific to the negative belief system. Implications for research and educational practice are discussed.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".