Deconstructing Dominant Narratives of Urban Failure and Gentrification in a Racially Unjust City: The Case of Detroit
Bibliographic record
Abstract
Abstract In Detroit, pockets of gentrification can be found amid larger processes of neighbourhood decline. Emerging gentrification is rapidly shifting the city’s dominant narrative from one of urban failure, to a comeback city. Both these interpretations, however, are problematic. In Detroit, race is central to understanding these narratives and the different meanings of gentrification. In this paper, I draw on in‐depth interviews with key visionaries and community leaders, all of whom share a broad concern for social justice. Two narratives that both challenge the dominant perspectives on Detroit become clear. The first sees gentrification is a necessary evil whose negative effects need to be carefully managed. The second is the perspective from many African American activists that gentrification is part of a continuum of racial discrimination. An analysis of these narratives helps to expose injustices, propose socially‐just solutions and politicise gentrification and its consequences, key elements of critical urban planning.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".