A Revisionist History Approach to the Study of Emotional Labor: Have We Forgotten Display Rules and Service Contexts?
Bibliographic record
Abstract
We take a revisionist approach to study emotional labor—commoditization of emotions for a wage—to delineate how organizational scholars must “revive and resubmit” two crucial elements of the emotional labor phenomenon that have been left behind as research within this space evolved. First, we argue that scholars have not paid enough recent attention to display rules that prescribe what emotions are acceptable within service interactions, instead assuming classic conceptualizations (i.e. show positive emotions and hide negative emotions) still prevail. Second, we highlight that the shift away from service occupations to more white-collar occupations may have minimized our understanding of the complexity of emotional labor in modern service arrangements, such as multiple job holders, and employees in the gig economy. We hope that future emotional labor scholarship will dig into several taken-for-granted assumptions about the phenomenon moving forward to help “go back to the basics” regarding display rules of those in service work.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".