Can a Coffee Shop be Abolitionist? \nBusiness–Police Relations in Halifax’s North End Neighbourhood
Bibliographic record
Abstract
As profit-seeking consumption spaces, sustained by wage labor, local businesses generally em-brace neighbourhood police practices. Yet, whether business-police relationships differ across business spaces, has been largely undocumented and unexplored within the academic literature. This research uses North End, Halifax as a case study to investigate how businesses influence po-licing practices both within their own space and within the neighborhood more broadly. Using abo-lition as my geographical method, I study business spaces as potential commons: sites for prefigu-rative politics, or abolitionist geographies in the making, while remaining attentive to the ways business spaces resist these framings, and instead map on to carceral geographies. I carry out this analysis in the form of a three-part audio documentary series, included here as three written scripts. Interviews with business owners, business association board members, and long-term res-idents, living and working in the North End between the 1960s and today, populate these scripts, showing how diverse actors have mediated and understood safety, and policing, across time and space. Altogether, this research situates neighbourhood businesses within the complex Canadian landscapes of carcerality, while also aiming to document how these same businesses might, and have been, sites of radical placemaking and abolitionist futures to come.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".