School Commercialism and the Fate of Public Schooling: What’s “Good” for America?
Bibliographic record
Abstract
n 1952 General Motors president Charles Erwin Wilson famously pronounced, What's good for the country is good for General Motors, and what's good for General Motors is good for the country. This outright equation of commercial institutions with national identity, of corporate good with the American good, is the central dynamic explored in both Alex Molnar's School Commercialism: From Democratic Ideal to Market Commodity (2005) and Kenneth J. Saltman's The Edison Schools: Corporate Schooling and the Assault on Public Education (2005). However, two major changes have occurred since Wilson's assertion a half-century ago: Many corporations have grown to unprecedented size, the largest overshadowing the economies of most nations (Lodge & Wilson, 2006); and, as these books demonstrate, the trend has spread from car making to education.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".