Philanthropy to the rescue? Detroit’s schools and urban policymaking under austerity
Bibliographic record
Abstract
As municipal governments in the US struggle under austerity, philanthropic elites have seemingly come to the rescue. Their money has not come without strings attached, however. By leveraging political contributions and donations to non-profits, philanthropists have moved beyond funding services and into the promotion of their preferred policies to cash-strapped municipalities. This has meant that the super-wealthy can now set the terrain of urban policy debates in cities struggling under austerity, ignoring democratic processes and often working to actively co-opt or stifle dissent. Through a study of the politics surrounding an impending bankruptcy of the Detroit public school system in the mid-2010s, this article provides crucial insights into the nature of elite-led urban policymaking under conditions of racialized austerity. Specifically, it focuses on how competing coalitions of liberal and conservative philanthropists used their wealth and influence to define the parameters of the policy debate over the future of Detroit's schools. In doing so, these coalitions constrained the ability of residents with alternative visions to participate in decision-making processes and promoted a market-based system of schooling that served Detroit students poorly. This result must be understood as facilitated by the city's context of racialized austerity, as manifested both through the financial crisis facing Detroit's schools and through the system of emergency management used to take over Michigan's majority-Black municipal institutions. These findings highlight that as philanthropic funding and influence have grown under conditions of racialized austerity, we must critically examine their effects on policymaking and on systems of democratic accountability.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".