Why Black‐Majority Neighbourhoods Are The Epicentre Of Population Shrinkage In The American Rust Belt
Bibliographic record
Abstract
Abstract Despite a clear association between the most African‐American neighbourhoods and overall population shrinkage in Rust Belt cities, few have explored the theoretical challenges that this poses. First, if racial prejudice is the key ingredient for this relationship, why would this result in overall population loss and not simply a reduction of White residents? Second, why if stigmatisation generates the pre‐conditions for in‐migration and investment – as the gentrification and urban ecologies literatures suggest – why would Black neighbourhoods not be the epicentre of population growth? This paper explores these questions through a theoretical synthesis of the residential choice, shrinking cities, and racial prejudice literatures. I rely on an intra‐city examination of neighbourhood change in Cleveland, Detroit and Pittsburgh. I argue that three factors in shrinking Rust Belt cities limit the application of more general neighbourhood change paradigms: (i) the persistence of racial prejudice; (ii) abundant housing supply; and (iii) housing stock characteristics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".