Bibliographic record
Abstract
Research on emotional labor has shown that workers who are required to feign emotions are more likely to suffer ill effects than those who are able to deep act their emotions. The authors argue that what may stand between surface and deep acting is workers’ ability to claim the kind of socially valued role that makes their enactment of emotional display rules seem consistent with that role. The authors draw on observations and interviews with workers in the debt settlement industry to show that men who were agents were able to claim that they were educating clients rather than selling to them. This made it possible for them to avoid feeling that they were taking advantage of customers who might have been better off without the service they sold them. Men were able to help clients in a way that did not conflict with their role as salespeople. Women agents, by contrast, were not able to style themselves educators. Instead, clients and employers expected them to adopt a therapeutic role with their clients. However, this role conflicted sharply with the expectation that agents be effective salespeople and forced women agents to feign feelings of care and optimism. Clients, coworkers, and employers, the authors show, shaped workers’ freedom to define their emotional labor in satisfying ways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".