Reform, Ideology and the Politics of <i>Waiting for ‘Superman’</i>
Bibliographic record
Abstract
In the United States, charter schools have been proclaimed as the potential solution for a host of problems for inner-city education, particularly as a cure-all for marginal achievement scores on high-stakes standardized testing. Several US states and cities (most prominently Texas, New York, Atlanta and Washington, DC) have embraced this neo-liberal vision of educational reform as a ‘miracle’ panacea and injected business-derived principles into schools to much fanfare and few authentic positive results. This article focuses on the rhetoric of market-driven solutions for school reform as exemplified in Davis Guggenheim's 2010 documentary Waiting for ‘Superman’. Contextualizing the film within a milieu of recent reform and ‘miracle’ solutions, the article attempts to highlight the inherent narrative character of the educational reform movement in the United States and its perpetual willingness to ignore previous structural failures in search of new solutions which typically mirror previous innovations in their fictive type.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.077 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".