“Why do you have to be a drag?”: An Exploration of 2SLGBTQIA+ Sudents’ Experiences of EmotionalLlabour in Ontario Public Schools
Bibliographic record
Abstract
This thesis explores 2SLGBTQIA+ students’ experiences of emotional labour in the Ontario public school system. Building on Arlie R. Hochschild’s (1983) concepts of “emotional labour” and “feeling rules” I explore the complex ways power and emotion shape school landscapes. I ask, 1. How do politics of emotions within Ontario public schools impact 2SLGBTQIA+ students?; and 2. What are 2SLGBTQIA+ students’ in Ontario schools experiences of emotional labour? Using a secondary data set from Tara Goldstein et al.’s LGBTQ Families Speak Out Project video interview archive (2014-2018), I analyze the impact of pervasive cis-heteronormativity, and hegemonic intersecting power broadly, on the emotional landscape and expectations in Ontario public schools. Further, I use the secondary data subset to examine various ways 2SLGBTQIA+ students engage in emotional labour, namely, withholding authenticity and explaining, and educating about queerness, while also recognizing the complex relationship between resistance and/as emotional labour. My analysis of the LGBTQ Families Speak Out Project data subset provides a snapshot into 2SLGBTQIA+ students in Ontario public schools experiences of emotional labour. This thesis seeks to deepen understanding around politics of emotion in schools in hopes of nuancing discourses on 2SLGBTQIA+ students’ emotional safety in schools.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| 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".