Bibliographic record
Abstract
In real-world classroom settings, teachers regulate their emotions on a daily basis contributing to a persistent disconnect between the emotions teachers actually experience and those they choose to express. Accordingly, teachers routinely hide or fake discrete positive and negative emotions in the classroom, with this "emotional labour" being associated with not only lower teacher motivation and poorer physical health, but also higher rates of burnout and attrition. The current dissertation represents a composite of three separate manuscripts including both a systematic review as well as large-scale empirical study with 1,086 teachers from across Canada conducted in collaboration with 22 teachers' associations and unions across seven provinces and territories. The first manuscript employed a systematic review and meta-analysis synthesizing the results of previous research concerning teachers' emotional labour, their antecedents, and influences on teachers' psychological, physiological, and behavioural outcomes. The second manuscript utilized advanced structural equation modeling with latent interactions to demonstrate empirical support for hypothesized effects of teachers' personal values, perceived school values, and value congruence on teachers' emotional labour behaviours and psychological adjustment. Finally, the third manuscript provided an intensive methodological perspective concerning the measurement of teachers' emotional labour by way of competitive latent models and a multitrait-multimethod analysis of a newly developed self-report measure of teachers' emotional labour with respect to various discrete emotional experiences. Study findings observed concerning the structure as well as antecedents, correlates, consequences of teachers' emotional labour across these dissertation studies make significant contributions towards better understanding of teachers' motivation, emotions, and emotion expression in the teaching context, and may serve to inform teacher development initiatives by underscoring the critical, multifaceted role of emotional labour for teachers, administrators, and students.
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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 | 0.029 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".