Neighbourhood Attainment and Residential Segregation Among Toronto's Visible Minorities
Bibliographic record
Abstract
Since the 1960s, the social complexion of Toronto's urban landscape has been irreversibly altered as new waves of migrants from Africa, Asia, the Caribbean, and Central and South America have replaced traditional white European migrant flows. This product examines the very different residential settlement patterns of Toronto's three largest racial minorities - Blacks, Chinese and South Asians. Unlike previous studies based on aggregate level data and 'ecological' correlations, this product assesses the capacity of conventional spatial assimilation theory to account for these differences, using 'locational attainment' models estimated with micro-data from the 1996 Census of Canada. Conclusions show that the residential settlement patterns of South Asians and, strikingly, Blacks fit the expectations of the conventional spatial assimilation model rather well. Initial settlement is in disadvantaged immigrant enclaves from which longer-term, more successful migrants subsequently exit as they purchase homes in more affluent neighbourhoods. Although Toronto's 'Black neighbourhoods' are decidedly poorer than other minority neighbourhoods, most Blacks do not live in these neighbourhoods. In contrast, Chinese immigrants move quickly to purchase homes in somewhat more affluent and enduring ethnic communities. This product shows that, rather than being historically novel, however, the Chinese are replicating the settlement pattern of earlier southern European (particularly Italian) immigrants and for much the same reasons (i.e., relative advantage in the housing market and low levels of language assimilation).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".