Secure in our Masculinities: A Phenomenological Investigation of Private Security Work in Ottawa
Bibliographic record
Abstract
This dissertation explains why people work in lousy jobs in the private security industry, as illustrated by a case study of this work in Ontario, Canada.This industry has experienced rapid growth worldwide for decades and is a significant and intrusive part of many lives.Private security worker jobs are precarious, usually poorly paid, offer few benefits and require long and erratic hours.It is a lousy job that requires both endurance and skill.The workers have less public respect and fewer resources available than the police, yet the industry has few problems attracting many workers.Some of these workers develop a solid attachment to the industry and strong emotional engagement with the work.Their attachment to the industry is strongly associated with specific ideas and practices of masculinity and this helps explain the continued dominance of men at all levels.They also engage in specific forms of emotional labour that helps give them feelings of self-worth about their role in protecting people from threats such as accidents, acts of violence, and loss of property.This combination helps perpetuate a specifically masculine concept of providing security.Using ethnographic methods and phenomenological analysis, I show how workers practice gender in the workplace and how the emotional labour required by their job discourages some people from working in the industry and attracts other.The resulting industry practices help formulate unique practices and ideas of security that are gendered, racialized and relevant on a global level.The industry's expansion deepens and expands the social understandings of security in contemporary society as a masculine project and this project shows how that occurs at the level of individual workers in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.054 | 0.036 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".