Bibliographic record
Abstract
Charter schools are publicly-funded entities that enjoy freedom from many of the regulations under which traditional public schools operate. There are, however, state and local variations in charter legislation and implementation. The Charter School Landscape offers a discussion and comparison of charter school politics, policies, and significant issues in eleven key U.S. states and the Canadian province of Alberta. The first charter school opened in Minnesota in 1992. Within nine years, there were more than 2,000 charter schools in operation across thirty-four states, Washington, D.C., and Alberta, Canada. Public discourse on charter school reform is often passionate and politically motivated. Sandra Vergari has assembled a group of experts to present a more reflective and scholarly discussion of the reform, its performance to date, and implications for public policy. Each of the twelve chapters focuses on a single state or province, and systematically address such issues as charter school laws, the politics of policy implementation, charter school accountability, controversies and trends, and prospects for the future. In addition, each chapter emphasizes significant issues specific to that state which offer lessons for analysts and policymakers everywhere. As a whole, The Charter School Landscape suggests that the reform is having a significant impact on the institution of public education and how we think about the concept of the real public school.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".