Bibliographic record
Abstract
What is Emotional Labour (EL)? Emotional labour is the process of managing and displaying emotions in the public sphere of our lives, most importantly the workplace, where emotional labour is connected to employment and is therefore done for a wage. In this doctoral thesis I explore how emotional labour is a human process that can be complicated by personal factors such as race. There were three critical questions posed in this research: (1) what is the nature of emotional labour in the work lives of Black teachers in this study? (2) how is emotional labour experienced in the work lives of Black teachers in this study? (3) what is the nature of the responses and strategies in relation to emotional labour that Black teachers in the study use in their work lives? A mixed methods design was used to gather data about the emotional labour experiences of Black teachers from two sources: (1) an online survey administered to 66 teachers from various racial groupings from the Greater Toronto Area; and (2) a series of in-depth interviews with seven Black teachers. The survey findings corroborated the experience of emotional labour for Black teachers and revealed three key concepts: racialized emotional labour (REL)—the additional emotional labour associated with being Black that prioritizes labour output; racialized emotional work (REW)—the additional emotional work associated with being Black that prioritizes human output; and white emotional privilege (WEP)—the emotional advantage gained by White teachers who are not required to engage in extra emotional labour/work attributable to race. The in-depth interviews suggested three types of teachers based on the ways they engage REL and REW: (1) Struggling to Resist; (2) Strategically Coping; and (3) Accommodating,Sticking to the Classroom, and Keeping-On. The research also provided foundational information on the development of copingstrategies among Black teachers and on the significance of spheres of interaction in the emotional experience of teachers. This complex and very rich approach gave a voice to Black teachers and focused on their personal perspectives
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".