*State Aid IDENTIFIERS *Wentworth County Board of Education ON
Bibliographic record
Abstract
Canadian education has traditionally been financed by two major sources: (1) transfer payments from the federal government to the provinces; and (2) residential and business property tax revenues levied by municipalities. The Canadian federal government has recently announced a planned cut of 750 million dollars a year for 3 years in transfer payments for education to the provinces. As a result, school board administrators must make stringent use of budgeting resources that have also recently been reduced at the provincial level. This paper examines the impact of incremental resource reductions on the Wentworth County Board of Education (Ontario), with a focus on maintaining teacher support and service quality in schools while retaining equity of educational provision. It is recommrInded that Ontario carefully restructure the way in which the resource interface is managed at provincial, school board, and school levels. Measures taken at the school board level will not deliver efficiency, effectiveness, economy, and equity. The continuation of existing administrative structures will result in an unraveling of educational provision, disparities in both the level and quality of services for students, and an inability to address a growing range of professional issues related to equity. The appendix contains the Wentworth County Board of Education budget for fall
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.479 | 0.127 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".