Policing and Fatherhood Identities: A Gendered Analysis of the Work and Home Experiences of Police Fathers Before and During COVID-19
Bibliographic record
Abstract
Policing scholars have long pointed to police culture as an explanation for the negative behaviour of officers (Chan 1996), yet police culture also plays a crucial role in informing how officers make sense of their experiences both inside and outside of the organization. Much of the research on gendered experiences of police culture have focused on the experiences of women police, yet little attention has been given to the experiences of male officers in macho police culture. Moreover, there is a paucity of literature that has focused specifically on how police who are fathers perceive their own experiences, either at work or at home (Pettigrew & Duncan, 2020 is one exception to this trend). To address this gap in the literature, I conducted a gendered analysis of police fathers’ experiences at work and at home. Moreover, the advent of COVID just prior to start of this research, provided an ideal opportunity to study whether, how, and to what extent the pandemic has created a space for cultural change within policing as seen through the experiences of police fathers at work and at home. Through in-depth interviews with 18 Canadian police fathers, I examine how police fathers experienced their lives at work and at home before the onset of COVID, how they are positioned in respect to their investments in ideologies about what it means to be a father, and how their experiences have been impacted by the pandemic. I argue that there is an apparent divide between the organizational experiences of men and women police with respect to navigating their identities as fathers and mothers, with fathers having a much more positive experience than mothers. Drawing on Campeau’s (2019) theorizing on generational boundaries and cultural scripts, I develop a typology that consists of four types of police fathers including: the ‘co-parent,’ the ‘organization man,’ the ‘entertainer,’ and the ‘regretful breadwinner.’ And lastly, applying Campeau’s (2015) conceptualization of culture as a resource, I demonstrate how officers utilize culture as a resource to downplay COVID risks with their colleagues, highlight COVID risks in the public, and demonstrate both an intensification and a transcendence of traditional gender roles with their families. In conclusion, I provide achievable recommendations for policing organizations and directions for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".