Putting on a good face: An examination of the emotional and aesthetic roots of presentational labour
Bibliographic record
Abstract
When we put on a good face we are claiming a set of approved social attributes – presenting an image of who/what we wish to be accepted as and taken for, by others. As Erving Goffman puts it, we have a good face when we fit an image others have of, for example, our profession, by making a good showing of ourselves (Goffman, 1967: 5). There is a large body of literature on the emotional labour of controlling and showing an emotional good face, that is, the work to preserve a professional and a corporate ‘face’, even if that entails hiding or disguising one’s personal emotions. Another smaller body of literature, building on the concept of emotional labour, is that describing aesthetic labour. Aesthetic labour is the selling of one’s embodied ‘face’, or approved social attributes, to create and preserve a professional and/or corporate image – often described as ‘looking good and sounding right’. Emotional and aesthetic literacy are fundamentally communication concepts requiring sophisticated perceptual as well as messaging skills. Using hairstylists as exemplars, I examine the close and personal relationships stylists enjoy with their clients as they toil, behind the chair but in the mirror, gathering insight into the relationship between emotional labour and aesthetic labour, and to the acquisition of emotional and aesthetic literacy that is essential to the effective performance of presentational labour.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".