Bibliographic record
Abstract
Taking as its focus the not-so-special case of Detroit, which recently experienced the largest municipal bankruptcy in US history, this article explores the financialization of American urban governance in both conceptual and concrete terms. The financially mediated restructuring of Detroit, through the imposition of emergency management by the state of Michigan and subsequently through the federal bankruptcy code, has been portrayed as an extreme event, with deep roots in histories of deindustrialization, racial exclusion, and suburban flight. It is not to downplay the significance of this experience to suggest, however, that the Detroit case also represents an ordinary crisis of a faltering regime of financialized urbanism. Compounding a shift toward entrepreneurial urban governance, cities now find themselves in an operating environment that has been constitutively financialized. Bondholder-value disciplines have become systemic in reach, along with an amplified role for financial gatekeepers like credit rating agencies; technocratic forms of financial management have been spreading and deepening, both in supposedly normal times and under externally imposed emergency measures; and in some cities the routinized play of growth-machine politics is being eclipsed by a new generation of debt-machine dynamics. While the ultimate focus of this article is on Detroit, its chief concern is with the framing of the city’s storied financial crisis—theoretically and then institutionally.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".