Women, mining and gender: experiences in Greater Sudbury
Bibliographic record
Abstract
My interdisciplinary research explores the gendered work experiences of women in \nmining. Statistics Canada confirms women’s unequal participation in the industry, and the \nMining Industry Human Resources Council reports that only about fifteen percent of the \nCanadian mining labour force are women. The literature attests that women often face challenges \nof acceptance in male-dominated, blue-collar industries. They disproportionately experience \ndiscrimination and harassment in industries in which they are the minority, yet the literature does \nnot fully address women’s work experiences in this industry and it is important to do so given \nmining’s important place in Canada’s economy, both nationally and regionally. My study \nexplores narratives about women’s experiences in this male workplace culture. In 2020, I \ninterviewed 35 people who work in the mining industry in the city of Greater Sudbury, Ontario \nto ask women (N=24) about their direct work experiences and workplace interactions, and men \n(N=11) about their work experiences and workplace interactions with women. I used methods of \nanalysis that “bricolaged” approaches of thematic and critical discourse analysis. My findings \nsupport the need for further initiatives toward equity, diversity, and inclusion, not only in mining, \nbut in other gender-imbalanced industries. Women described how they experienced resistance to \nthe achievement of acceptance and respect at work. Many experienced harassment and \ndiscrimination, and spoke about the masculine organizational culture present in their work \nenvironments. Nevertheless, they also described job satisfaction in the work that they perform, \nand described bonds of kinship with peers. However, these bonds were usually described in \ngendered terms. Women revealed that the camaraderie they seek most to achieve is to be “one of \nthe boys” or “one of the guys.” At the same time, they spoke about bonds of “sisterhood” in \nmining, and how the mining industry offers a space where they celebrate alternate expressions of \nfemininity, such as being a “tomboy.” Men confirmed that resistance toward women in mining \nexists, and that notions of gender essentialism continue to impact perceptions about traits linked \nto men and women. In sum, my study reveals that the masculine organizational culture of the \nmining industry is complex. The purpose of my interdisciplinary, community-based study was to \nunderstand this complexity and offer solutions for creating more equitable, diverse and inclusive \nwork cultures within the industry for all workers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.040 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".