The emotional labor automation model: Towards the unmanaged heart
Bibliographic record
Abstract
Emotional labor – the act of expressing or managing emotions to conform to rules and norms at work – is experienced in virtually all professions. Researchers have readily examined how the strategies of deep and surface acting impact employee wellbeing and performance. To a lesser extent, genuine emotional expression has been studied as a fruitful third form of emotional labor. Research to date has shown generally salubrious outcomes of genuine emotional expression over deliberate, effortful strategies. However, research has yet to explore if and how genuine emotional expression can develop over time. We proposed the Emotional Labor Automation Model as the first developmental framework to explore how effortful practice can trigger automatic emotion regulation (AER) and develop emotional labor habits. We suggest three categories of antecedents (i.e., job, employee, and job-employee interplay factors), which lead employees to develop distinct trajectories of emotional expression styles in the workplace, including genuine emotional expression in line with display rules. We explain how each resultant emotional expression style promotes a range of maladaptive and adaptive employee wellbeing and performance outcomes. A discussion of mechanisms, moderators, and key areas for the future of temporal emotional labor research is provided.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".