“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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".