From Tax Fairness to Social Justice: Mapping Rules and Money at District Entry
Bibliographic record
Abstract
This working paper marks the consolidation of my first-term doctoral research direction in Social Justice Education. It conceptualizes education access as fiscal architecture, focusing on how rules and money are organized at the point of school entry. The analysis distinguishes two directional movements. The X dimension captures horizontal fiscal movement across jurisdictions, specified as X₁ (cross-border and inter-jurisdictional movement of pre-tax capital and legal obligations) and X₂ (within-jurisdiction redistribution of post-tax public resources). The Y dimension traces intergenerational post-tax capacity, including time, care, and knowledge, which shapes families’ ability to navigate entry requirements across generations. Using qualitative document analysis and process tracing of publicly available Ontario school-entry materials, the paper reconstructs entry pathways to identify where verification requirements, fiscal cues, information visibility, and capacity-based branching appear in sequence. The contribution is descriptive rather than causal or prescriptive. It provides a replicable, node-level structural mapping of how formally uniform rules can yield unequal access conditions when redistribution interfaces and household capacities are misaligned. The paper is positioned as a foundational methodological and theoretical piece for subsequent comparative and mixed-methods research in social justice education, public finance, and education policy.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".