Challenging Racial Silences in Studies of Emotion Work: Contributions from Anti-Racist Feminist Theory
Bibliographic record
Abstract
Little or no attention has been paid to the racialized dimensions of the emotion work done by individuals as part of their paid jobs. I argue that this exclusion of racial analyses is symptomatic of a static conceptualization of the subject underlying many studies of emotion work. While theorists illuminate the different forms of emotion work required by women and men, and by individuals in various professions, there is little understanding of the relationship between the emotion work people do and their social locations within interactive race, class and gender hierarchies. Drawing on feminist anti-racist theory I propose a multidimensional approach to difference and stratification, which would allow us to illuminate new forms of emotion work done by people living in today's heterogeneous social and economic context. The theoretical discussion in this article is complemented by an analysis of the experiences of an ethnically diverse group of women who are small-business owners in Halifax, Canada.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.026 | 0.108 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".