Bibliographic record
Abstract
Approximately 12.5 % of the overall education funding, the special education grant increased from $1.6 billion in 2002-03 to $3.2 billion in 2020-21. For equity and inclusion, demands to increase the special education funding continue. Students with exceptionalities are at risk of lower achievement. All schools must provide special education programs. However, there has been no study investigating the special education grant per se. The purpose of this study is to examine how the special education grant for elementary and secondary students with exceptionalities in Ontario, Canada, is determined. The research questions are: How is the special education grant determined? How is funding for different exceptionalities determined? Document analysis is the main method for this study, but the author has also contacted the Ministry of Education for information not available through open documents. This article reviews funding information since 1998 and indicates that the special education grant increases almost annually. It is decided with a variety of mechanisms with six components. Three are determined mainly by total enrollment and three are determined mainly by claimed cases for different exceptionalities. The article helps us understand how the special education grant is determined, informing the discussion on policies of funding for students with exceptionalities.
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 | 0.018 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".