Bibliographic record
Abstract
When condominiums first emerged in North American cities in the 1960s, they were a new kind of housing governed by boards of resident owners volunteering in a community. Condo Conquest shows how the condo and its inner governance have since become something else entirely, taken over – or conquered – by an assemblage of firms specializing in condo law, real estate, security, and property management, as well as growing numbers of non-resident investors who purchase condo units as commodities. Drawing on the accounts of residents and board directors in Toronto and New York and myriad other sources, Randy Lippert takes a close look at the inner workings of condoization. He shows how condo governance increasingly involves a complex set of legal, social, and spatial relationships among various elements assembled together, including commercial agents, forms of knowledge, and technologies. The first major study of condominium governance in North America, Condo Conquest questions assumptions about the condo and its governance. By illuminating the complex set of agents, processes, and forms of knowledge that have taken over the condo world, Lippert discerns a number of troubling trends that imperil the condo’s future and undermine the integrity of urban communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".