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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".