In the Circle of Fire: Gendered Barriers in Fire Services in Ontario
Bibliographic record
Abstract
The firefighting profession is described as inherently dangerous, rich in pride, honour and tradition. Firefighters are held in high regard, as they are known for their involvement in, and commitment to the community. Firefighting is a ‘public safety’ service, with a labour force that is predominantly white males. The public expect firefighters to fight fires and rescue those in distress, displaying heroism, strength and embodying masculinity (Yarnal et al., 2016). Although described as a masculine profession, the role of the firefighter is changing, and the composition of the service is beginning to evolve to reflect the community that it serves. This phenomenological study, guided by the principles of standpoint theory, investigates gender-based workplace dynamics within firefighting, uncovering ways in which nuanced stereotypes, bias and discriminatory practices contribute to a less inclusive and sometimes unsupportive environment for women in the Fire Services in Ontario. Thirty-two firefighters participated in semi-structured interviews. The themes presented are generalized to both genders, as well as themes unique to either male or female firefighters. This study’s findings reveal that while some themes are found to apply to both genders, others are distinct to women firefighters. This dissertation highlights the negative impacts the workplace has on women firefighters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".