Are Immigrants Buying to Get In?: The Role of Ethnic Clustering on the Homeownership Propensities of 12 Toronto Immigrant Groups, 1996-2001
Bibliographic record
Abstract
Numerous studies equate immigrant homeownership with assimilation into the residential mainstream, though only rarely is this claim verified by studying the ethnic character of neighbourhoods where immigrants actually buy homes. In this paper, the 1996 and 2001 Census of Canada master files and bivariate probit models with sample selection corrections (a.k.a. Heckman probit models) are used to assess the neighbourhood-level ethnic determinants of homeownership in Toronto, Canada. By determining whether low levels of ethnic concentration accompany a home purchase, it can be assessed whether immigrants exit their enclaves in search of a home in the 'promised land', as traditional assimilation theory suggests, or if some now seek homes in the 'ethnic communities' that Logan, Alba and Zhang (2002) recently introduced in the American Sociological Review. Assessing the role of concentration under equilibrium conditions, evidence emerges that same-group concentration affects the propensity of several group members to buy homes.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".