Governing Incivility: An Ethnographic Account of Municipal Law Enforcement, Urban Renewal and Neighbourhood Conflict in the City of Hamilton
Bibliographic record
Abstract
This dissertation offers a detailed ethnographic account of municipal law enforcement in the City of Hamilton, Ontario, Canada. Based on data collected from over 600 hours of ethnographic observations (ride-alongs) with 18 different municipal law enforcement officers and 20 semi-structured interviews conducted with city planners and municipal law officials, this study explores the inconsistencies between how municipal law enforcement is prioritized and how such enforcement plays out throughout Hamilton’s socio-economically diverse neighbourhoods. This dissertation will therefore argue that despite the presence of a neoliberal redevelopment agenda outlined in City documents, discussed by planners and some municipal law officials as well as clearly visible in the privately funded development projects restructuring Hamilton’s downtown, most municipal law enforcement is reactive, serving the function of managing neighbourhood conflicts rather than addressing the physical aesthetics and perceptions of safety in the downtown core that are in keeping with neoliberal economic agendas. As part of this argument, this research demonstrates how while the law itself is not irrelevant, the investigation of local disputes often expose tensions that extend far beyond the scope of municipal law. This study will conclude by exploring how the enforcement of some laws, although carried out in the name of health and safety, can and do have serious social implications for those living on the margins. This discussion will occur within a broader context where it will be argued that as the physical and social landscape of Hamilton continues to change, planning and municipal law officials as well as officers must remain aware of the potential detrimental effects that the enforcement of some municipal laws can have on certain sectors of the City’s population.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".