Bibliographic record
Abstract
Making Schools Work: A Revolutionary Plan to Get Your Children the Education They Need by William G. Ouchi (with Lydia G. Segal). New York: Simon & Schuster, 2003. 284 pp. ISBN 0-7432-4630-6 William Ouchi’s thesis is simple: “If the district is run properly, all of the schools in it will be successful. If not, all schools will suffer, and only those principals who are willing to buck the central office will succeed” (p. 11). In his latest book, Making Schools Work, the UCLA management professor argues that the effects of decentralized decision-making and a culture of entrepreneurship will be powerful enough to stimulate district-wide gains in student achievement. In keeping with the business management genre to which this book belongs, Ouchi contends that seven key elements can propel any mediocre school district to success. They are: every principal is an entrepreneur; every school controls its own budget; everyone is accountable for student performance and budgets; everyone delegates authority down the line; there is an intense focus on student achievement; every school is a community of learners; and families can choose from a variety of schools. Ouchi and his researchers derived these seven elements from a comparison of a range of school districts, their principals, and their decision-making systems. His sample includes three large, centralized school districts (Los Angeles, New York, and Chicago); three decentralized districts (Houston, Seattle, and Edmonton, Canada); three large Catholic school districts; and six independent schools. He and his team interviewed principals, observed classrooms, and visited district headquarters. They studied their management systems, budgets, and student performance. The author’s general premise is a worthy one. Attention should be paid to the powerful role of the district. Attempting to reform individual schools without considering the district context may ultimately be in vain. Yet when embarking on new reforms, such efforts must be tempered by the lessons to be learned from decentralized school systems at the turn of the century, which, left unchecked, grew rife with corruption, nepotism, and objectionable hiring practices (Tyack, 1974). While it is not perfect, we need to appreciate the underlying rationale behind today’s centralized school districts when evaluating school management proposals like Ouchi’s. Educational researchers and practitioners often jump at the chance to criticize businesspeople for contending that if districts looked more like commercial enterprises, they would see improvement. Clearly, both educational and business institutions have important lessons to offer one another. More troubling to me, however, are the assumptions implicit in Ouchi’s argument. As with most reforms that promise sweeping change in our schools, the devil lies in
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.048 |
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".