Tears at Work: Gender, Interaction, and Emotional Labour
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For a long time, it has been believed that it is possible to leave our emotions at the threshold of the workplace. This excessively simplifies the complexity and heterogeneity of work, leading to an underestimation of the effects of work on health. Our objective is to understand one particular form of the expression of workers’ emotions: crying at work, which may be linked to an excess of emotional labour or to the impossibility of its achievement. Thus, differences between male and female crying, at least at work, may be explained not only by a gendered socialisation of individuals, but also by the sexual division of emotional labour. This imposes an emotional overload on women, since a more intensive management of emotions is demanded of them at work. Nous avons cru pendant longtemps qu'il était possible de laisser nos émotions à la porte des organisations. Cela simplifie excessivement la complexité et l'hétérogénéité du travail et, par conséquent, on finit par sous-estimer les effets du travail sur la santé. Notre objectif est de comprendre une forme particulière de l'expression des émotions des travailleuses et travailleurs : les larmes au travail qui peuvent être associées, soit à une surcharge de travail émotionnel, soit à l’impossibilité de son accomplissement. Ainsi, les différences entre les larmes des femmes et des hommes, au moins au travail, peuvent être expliquées, non seulement par les différences sexuées dans la socialisation des individus, mais aussi par la division sexuelle du travail émotionnel et des émotions qui impose une surcharge émotive plus prononcée aux femmes en demandant une gestion plus intensive de leurs émotions au travail.
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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 | 0.000 | 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) | 0.007 | 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 it