Gendering Social Relations of Work in the Canadian Automotive Industry: An Autoethnographic Study
Bibliographic record
Abstract
This paper is an analysis of my gendered experiences working in the male-dominated industry of automotive manufacturing. My objective was to explore and critically analyze the gendered relations that I experienced while working in the contemporary automotive industry. Women working in male-dominated professions and environments often face circumstances that are unique to their male counterparts (Acker, 2006; Gottfried, 2013). The nature of the social relations affects women’s integration and potential success in male-dominated professions (Acker, 2006; Gottfried, 2013). \nThe purpose of this research was to explore the challenges that I experienced as a woman working within a male-dominated profession. This was an exploratory qualitative study which was conducted through the method of autoethnography. An investigation and examination of my journal allowed for me to select important themes that represented the gender relations I experienced in the workplace. The main findings indicated that while working in male-dominated profession I experienced a host of challenges that were inherent in my work setting. The main challenges were as follows: 1) negotiating dominant forms of masculinity, 2) gender stereotyping/gender role expectations, 3) sexual harassment, and 4) the ways in which women’s bodies were considered suspect. I adopt several concepts/ideas from feminist political theoretical perspectives as well as other literature to analyze these themes. There were a variety of different concepts and theories that could assist in explaining why I was treated in an oppressive and dominating fashion. These concepts were as follows: 1) hegemonic masculinity, 2) gendered division of labour, 3) gendered hierarchies at work, and 4) embodied labour and bodies at work (Acker, 2006; Bakker & Gill, 2003; Bird, 1996; Connell, 1995; Gottfried, 2013).
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.035 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".