Bibliographic record
Abstract
Listening to the voices of people like Grace Lee Boggs, it is easy to be inspired and filled with a sense of hope. Hers was a powerful voice projecting an alternative narrative to that of urban failure. The initiatives that have grown out of conversations at the Boggs Center, or through the passionate visions and tireless work of activists and community and civic groups, offer genuine alternatives for a more inclusive, fair, and socially just future. I am honored and privileged to have been able to share conversations with people like Yusef Bunchy Shakur, Malik Yakini, Sandra Hines, and the many others who are involved in initiatives that are making a real difference to Detroit and its residents. These voices offer an alternative to the narrative that dominated Detroit for decades, that of decline and abandonment. But they also need to be placed in context. Contributions by George Galster, Ren Farley, Joshua Akers, John Gallagher, and others offer sobering reality checks as to the severity of Detroit's challenges and the inability of its current political and economic systems to tackle some of the city's biggest problems. Only by highlighting complex and diverse perspectives can simplistic narratives be challenged and the contradictions of Detroit's economic, social, racial, and political conditions be revealed. Yet, Detroit and many other cities around the world are still portrayed in simplistic, one-dimensional narratives, which is why scholars, practitioners, and others need to continually challenge those messages by asking difficult questions and offer alternative ways of interpreting the city. In Detroit, the dominant narrative for decades was as a metonym for urban failure. The peak of this narrative came in the years after 2008, with the global financial crisis, the US subprime mortgage crisis, and the bailout of the auto industry: Detroit's prominence as the poster child for urban decay took it to a world stage. Filmmakers, journalists, photographers, and academics from around the world put Detroit's problems into global limelight. It was in these years that numerous coffee-table books depicting the city's ruins were published (Austin and Doerr, 2010; Marchant and Meffre, 2010; Moore, 2010; Taubman, 2011), as well as films such as Detropia , which relied heavily on ruin imagery.
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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 | 0.001 | 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 | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".