Fair in Unfair Circumstances: Police Officers' Perceptions of Homeless Encampments
Bibliographic record
Abstract
This thesis presents local police officers’ experiences with Edmonton’s homeless encampments and the unhoused population. For this project, I asked two research questions: 1) How do Edmonton Police Service officers reflect upon, perceive, and express their interactions with homeless encampments and individuals?, and 2) How do these accounts inform our understanding of the dynamics with policing marginalized communities? I interviewed 23 police officers who were currently policing homeless encampments or had previously worked with the unhoused population. My thesis demonstrates two broad themes related to police officers’ descriptions of police-unhoused interactions. First, officers are frustrated with the problem of homelessness. They were particularly frustrated with current homelessness strategies that enable homelessness while depleting policing resources. Despite these frustrations, officers learn how to manage the city’s homelessness crisis through their interactions with encampment residents, often demonstrating ways to be ‘fair in unfair circumstances.’ This thesis also demonstrates the range of new technologies used by officers: the benefits of these technologies and how officers’ perceptions of these technologies impact police-unhoused interactions. These findings provide important insight into police officers’ conceptualizations of this social dilemma.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".