Condo Crimes and Legal Prospects for Confronting the Unusual Suspects
Bibliographic record
Abstract
Abstract This chapter seeks to classify condominium crime, explain its neglect in light of the growth of condo living in cities and closely consider the prospects for greater visibility and legal regulation of these acts. We deploy traditional dichotomies of white-collar/street crime and insiders/outsiders to construct a two-dimensional typology of condo crime and illustrate each type using empirically grounded examples from extensive qualitative research in Ontario and New York State entailing analysis of media accounts, condo owner association and corporation websites, and numerous interviews with owners, board directors and industry actors. We argue that the condo form retains peculiar characteristics that tend to prevent public reporting of condo crimes and leaves the ‘usual suspects’ (i.e. street criminals) in the spotlight while other, potentially more damaging, acts are neglected. We conclude by discussing barriers to knowing the extent of condo crime and their relationship to legal regulation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".