Racialized Emotional Labour: The Weight of Blackness in White Spaces
Bibliographic record
Abstract
At the age of 17, from April 2016 to September 2016, I worked part-time at a yacht club on Toronto Island as a maintenance worker. I worked alongside another individual in the maintenance department, and we were both of Afro-Jamaican descent. The club had a predominantly white membership, with few customers who were people of colour. The staff was also mostly white, and there were only five other people of colour who worked there besides us, and none of them were black either. I found that, while interacting with members, I faced racialised remarks and assumptions based on my position as a maintenance worker and as a young black woman. To remain professional and avoid validating any of their racist assumptions, I employed a high level of emotional labour and restraint. In discussions with my Jamaican colleague, I found he faced similar racialised comments; he also felt it necessary to employ emotional control to uphold a palatable image. However, I also found that the non-black employees did not employ the same level of emotional labour. This is not an isolated experience. I have also had to engage in emotional labour in other workplaces. Moreover, it is common to hear about Black employees, especially Black women, performing emotional labour for non-black customers. Black female employees must employ more emotional labour when working in predominantly white spaces, especially in racialised occupations.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".