Tearing down the city to save it? ‘Back-door regionalism’ and the demolition coalition in Cleveland, Ohio
Bibliographic record
Abstract
In this paper, we explore what Cleveland, Ohio’s program of demolishing abandoned and foreclosed houses can teach us about the logics and politics of post-2007 austerity urbanism. We investigate the emergence of a local political coalition that has promoted demolition as a solution to the city’s housing crisis. In tracking how this political consensus is spatialized, we analyze how demolition both supports and complicates existing theories of austerity urbanism. We theorize demolition as a spatio-temporal fix, a locally negotiated response to the larger-scale political-economic limits imposed by neoliberal austerity. This fix occurs at both the neighborhood level, where demolitions clear land for future reinvestment, and at the regional level, where the increasingly more-than-urban nature of the US housing crisis allows demolitions to gain regional support among fragmented municipalities. In the seemingly paradoxical pursuit of demolition as a growth strategy, political actors in cities like Cleveland aggressively push to tear down the city in a desperate attempt to save it.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".