The moral imperatives of geographies of school failure: Mobilizing market‐based reform coalitions
Bibliographic record
Abstract
School reform efforts in the United States often declare the entire institution irreparably unresponsive to student needs and in need of a fundamental overhaul. Consequently, coalitions that incorporate business and philanthropic actors have advocated strongly for the introduction of market logics into the public system. I argue that these movements are facilitated by creating a sense of obligation that is mapped onto the space of the urban or metropolitan area. I draw on archival research and open‐ended interviews to demonstrate the degree to which this moral obligation dominates reform efforts in the Seattle area through two coalitions: the first, made up of local business, philanthropic, and citizen groups that advocate for introducing market logics of choice and accountability, and the second, made up of parents who advocate for a network of alternative schools that meet the needs of students not served by the public system. Based on these differences, I argue that the former coalition's greater influence is in part due to its ability to mobilize an image of urgency around school reform that is geographically specific even as it glosses over differences in the spaces of education quality within the larger Seattle area.
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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.037 | 0.047 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| 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".