A primer on formula funding: A study of student-focussed funding
Bibliographic record
Abstract
Formulas by which public funds are allocated to either schools or school boards are not new. They are phenomena that are closely connected to the rise of public systems of education and the recognition, in terms of public policy, that education increases the value of human capital (Boss and Levacic, 1999). Formulas are also particularly associated with political jurisdictions, like Canada, in which responsibility for education is located at more than one level of government. Formulas, as a means of allocating public funds for education, are neither universal nor required. There are alternatives to funding by formula. Less than one-half of American states, for example, use funding formulas exclusively (Thompson, Wood, and Honeyman 1994). Alternatives to funding formulas are numerous: equalization grants, foundation grants, flat grants, “categorical ” grants that are earmarked for special purposes, "percentage equalization" grants that match local funding on a proportional basis, guaranteed tax bases, some funding schemes and composites of two or more other types of grant. The permutations and combinations are nearly unlimited (Jones, 1971; Brimley and Garfield 2002). In Ontario, in response to recommendations of the Committee on the Costs of Education (1978), provincial
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".