Metal at urban margins: Regulating scrap metal collecting in Winnipeg, Canada
Bibliographic record
Abstract
Scrap metal theft and collection has captured the attention of criminology and criminal justice scholars. Mainstream criminological research on scrap metal theft is focused on opportunity theory, arguing that theft can be reduced through stringent regulation of buying and selling by salvage yards. Alternatively, cultural criminology has examined the issue in ethnographic research exploring dumpster diving and scrounging. Additional conceptual tools are needed to analyze regulation of urban metal collecting, which leads us outside of criminology. The present study draws from urban studies and socio-legal studies to conduct a case study of the policing and regulation of scrap metal theft in Winnipeg, Canada. Using multiple methods including interviews, observations, analysis of news media and municipal regulations, we examine how scrap metal collection and processing is regulated in the city. We found four layers of regulation and law: federal, provincial, municipal laws, and what we refer to as the law of the lane. Our analysis contributes to literature on urban scrap and metal collecting and well as socio-legal literature on urban forms of regulation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".