Othering of Full-Time and Volunteer Women Firefighters in the Canadian Fire Services
Bibliographic record
Abstract
Being discriminated against because of factors such as gender, ethnicity, age, sexual orientation, and stature (i.e., height and weight) has been a common experience for women in traditionally men-dominated/identified occupations. Although women’s representation has risen in other men-dominated domains (Hughes 1995), within firefighting their presence remains extremely low in Canada (4.4% [Statistics Canada 2017]). Women firefighters mostly operate in a patriarchal context; they are often ignored, harassed, and treated poorly due to an intersectionality of factors (Paechter 1998). Thus far, most research has taken place in the US, UK, and AUS. In the present Pan-Canadian study, we examined the experiences of volunteer and career women firefighters (N=113). The Psycho-Social Ethnography of the Commonplace methodology (P-SEC [Gouliquer and Poulin 2005]) was used. With this approach, we identified several practices, both formal and informal (e.g., physical and academic standards, gender roles), which resulted in women feeling the effect of the intersection of gender and firefighting. Results indicated that women firefighters experience “Othering” manifesting itself in a variety of ways such as discrimination, hostility, and self-doubt. This paper focuses on Canadia women firefighters and ends with social change and policy recommendations to better their reality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".