Bibliographic record
Abstract
Purpose The purpose of this paper is to understand, from the child care worker's perspective, how work experience, display rules, and affectivity are related to emotional labor. It also examines the utility of separating surface acting into its two components: the hiding and faking of emotions. Design/methodology/approach This study is based on a cross‐sectional self‐report survey of 198 child care workers in Western Canada. Findings Deep acting occurred more frequently among younger workers, whereas experienced workers hid their feelings more frequently than did their less‐experienced counterparts. The requirement to express positive emotions was associated with deep acting and faking emotions, whereas the requirement to suppress negative emotions was associated with hiding feelings. Research limitations/implications Results support the treatment of surface acting's components as distinct given their differential association with the other variables. Future research should validate the emotional labor measure in service occupations that involve different frequency and intensity levels of contact. Practical implications The finding that young and inexperienced workers appear to engage in different emotion regulation strategies than mature and experienced workers may be due to their job training. A potential solution is to include service learning projects in child care training that build their confidence in communicating with parents. Originality/value Use of the revised Emotional Labour Scale in future studies may facilitate a deeper understanding of workplace emotional expression.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".