Legal Knowledges and Surveillance in the Condo World
Bibliographic record
Abstract
This article explores legal knowledges and surveillance in residential condominiums, a form of property ownership and collective governing arrangement that is proliferating globally. Drawing from extensive empirical qualitative study of this realm in Toronto, Canada and New York, USA, including interviews with condo board members, owners and industry representatives, we map various legal knowledges and forms of surveillance and how these relate to condo governance and condo life. We demonstrate how surveillance is enabled by various legal knowledges flowing into the condo world, including those stemming from an evolving condo statute in the form of ‘counter-law’, but also from civil law, municipal law and criminal law. We show these legal knowledges have spawned video surveillance, key fobs, human surveillance, reserve fund studies, financial audits and safety inspections that together form a governing assemblage of private actors. This surveillance is largely focused on board and owners’ practices in relation to financial viability and property value, which has accompanied the condo becoming foremost an investment rather than a residential community. Much relevant legal knowledge and surveillance is increasingly commodified rather than developed or provided by the community, thus underscoring that the condo is an ever expanding conduit for the flow of such commodities. The article concludes with a discussion of several implications of this analysis, including the notion that the condo world may be experiencing a spiral of more and more law and surveillance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".