“Because We Care ... It’s So Easy to Exploit Us”: Exploring Emotional Labour of Women Head Coaches as Invisible, Gendered Work
Bibliographic record
Abstract
In Canada, only 12% of University head coaching jobs are held by women. This percentage further decreases when considering the intersections of race, class and ability (Canadian Women & Sport, 2020; Norman et al., 2021). Sveinson et al. (2022) suggested that women’s emotional, invisible and unpaid labour may be contributing to gender inequity in sport; however, there remains a significant research gap regarding the working realities of women coaches. The purpose of this research was to critically explore the emotional labour of women coaches in the Canadian University context to develop a greater understanding of women's work as coaches. A post-structural feminist theoretical lens was used to challenge taken-for-granted gendered assumptions embedded within coaches' work in Canadian Universities. Two in-depth, semi-structured interviews were conducted with nine white, cis-gender, able-bodied head coaches of university, intercollegiate teams across Canada. Braun and Clarke's (2006, 2019) reflexive thematic analysis methods were utilized to analyze interview data and to gain insight into how women coaches use and experience emotional labour. It was found that women coaches use emotional labour to navigate a double-bind of display rules, based on both hegemonic masculine constructions of the coach as a heroic leader and essentialist feminine constructions of women as caregiver, while also using emotional labour as a tool to effectively manage relationships, create supportive environments and meet athletes’ needs both in and outside of sport. These findings contribute to a broader definition of coaching that encompasses the emotional realities of coaching from a feminist perspective.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.030 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".