Funding Mechanisms, Cost Drivers, and the Distribution of Education Funds in Alberta: A Case Study
Bibliographic record
Abstract
This article examines the impact that the 1994 funding changes introduced by the Alberta government have had on the Calgary Board of Education (CBE)—the largest urban board in Alberta and one of the largest boards in Canada. Starting from a critical financial analysis perspective we ather, examine, and recalculate key historical financial data pertaining to the CBE, contextualizing these data through the use of supplementary nonfinancial archival materials. Our analysis highlights the impact that funding changes have had on the CBE, but also indirectly tells us something about the impact on other school boards in the province, because the total amount of per-student education funding has remained relatively constant. More generally, the analysis illustrates how funding mechanisms can be and are used to govern from a distance and how seemingly neutral accounting/funding techniques function to distribute resources among different school boards. By drawing attention to these distributional effects, the current study makes visible the power of largely invisible funding mechanisms in the sphere of public education.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".