Implementing a Student-Based Funding Policy: Considerations for School Districts.
Bibliographic record
Abstract
SBF policies replace the traditional district budgeting model in which the central office allocates resources to each school based, for the most part, on what centraloffice staff believe is needed. In contrast, SBF policies allocate funds to schools based on individual student need, with the goal of increasing the equity of funding. In addition, SBF policies give schools greater decisionmaking power, with the notion that school staff, parents, and community members may be better situated than district staff to align resources to students’ needs. Several large urban school districts—beginning with Edmonton in Canada in the 1970s and followed by Cincinnati, Hartford, Houston, Oak land, San Fran cisco, and Washington, D.C., in the 1990s and 2000s—have implemen ted SBF models. Most re cently, in 2007, New York City, the nation’s largest school district, adopted its own version of an SBF policy, called “Fair Student Funding.” However, SBF policies are not without controversy; after using a weighted student formula for almost a decade, Seattle recently returned to a more traditional budgeting model. Still, many other districts around the country are considering implemen ting an SBF policy. Our recent study describing the implementation of two districts’ SBF policies—San Francisco’s weighted student formula policy (be gun in 2001–2002) and Oakland’s results-based budgeting policy (begun in 2004–2005)—revealed that both districts overwhelming preferred SBF policies over the traditional budgeting model. This preference is even more impressive since SBF policies require more work for both school and district officials. Given that school districts around the country are considering this policy, this article outlines eight key considerations that districts face when designing and
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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 | 0.048 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".