Follow the Money: Charter Schools and Financial Accountability
Bibliographic record
Abstract
Charter schools are an important and growing part of the nonprofit sector but the financial accountability and governance of the schools have received little attention from regulators or scholars. Highly publicized scandals of nonprofits have sparked strong interest in governance of nonprofits generally and have led to increased regulation. Charter schools receive more than $9 billion in public funds annually and the risk of improper use of that money merits attention. Although the charter school movement and the concerns it raises are national, this article focuses on Philadelphia as an example. In 2011-2012, one quarter of the public school students in Philadelphia attended charter schools. The Philadelphia School District provided $525 million to the 82 charter schools in the city. The vast majority of those schools also received funds from the state and federal governments as well. The District is expecting the percentage of students in charters to increase to forty percent in the near future. The article examines the weaknesses of the existing oversight system which relies primarily on disclosure of information to the School District and other governmental agencies, all of which lack adequate resources to respond effectively to the disclosures. The goal is not to enter the debate about the educational value of charter schools but rather to focus on how the schools fit into the larger debate over governance of nonprofits. Charter schools share the same challenges of overreliance on disclosure instead of enforcement of rules, insufficient education and training of board members, and a lack of transparency. Nineteen Philadelphia charter schools have been the subject of criminal investigations by federal authorities, resulting in seven convictions and one suicide. The article reviews the issues raised by the available documents which raise questions about the salaries of school officials; the complex relationships between many schools and their founding agencies; the widely varying expenditures for legal representation, accounting, and management; and the concerns about conflicts of interest raised in some cases. The article proposes increased funding for oversight, use of more nuanced tools than just revocation of the charter, greater transparency by the schools, and greater emphasis on board training.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 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".