Bibliographic record
Abstract
Urban studies scholars have explored the relationship between anti-Black residential preferences and segregation for nearly 50 years in the United States. The classical conception was that Black-white segregation was created and reinforced by a mix of anti-Black preference, discrimination, and poverty. Recently, scholars have been puzzled about why open anti-Blackness has diminished, but segregation has not. The compelling explanations for this turn are useful, but of limited applicability for cities outside of the United States in the Global North. In places such as Paris, London, and Toronto, substantial Black populations are of relatively recent origin, so some of the historical and social drivers of American segregation do not exist in the same form there, even if anti-Blackness does. This paper explores anti-Black residential preferences in Toronto using a 2,314-person online panel. I argue that the racial capitalism paradigm provides a more flexible and robust way to interpret the consequences of anti-Black preferences than segregation metrics. Housing markets are a primary mechanism for materializing racism. At times, that takes the form of segregation, but at other times it assumes different, but equally material, forms.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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; 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".