“Dollars Versus [Equality] Rights”: Money and the Limits on Distributive Justice
Bibliographic record
Abstract
The social benefit challenges under the Charter’s equality guarantee offer insight into the Supreme Court of Canada’s approach to claims for distributive justice. In most of these cases, the financial costs to government of rights recognition play a role in the analysis. A survey of the outcomes of these cases and the “dollars” at stake reveals a “follow the money” pattern. In all cases in which the claim that the government regime creates an inequality was successful, the cost to the public purse of finding in favour of the equality claimant was characterized by the Court as low or inexpensive. All those cases in which the public cost of recognition was regarded as significantly high have failed, although some inexpensive claims have also failed. The correlation suggests that a minimal budgetary impact is a necessary, albeit not sufficient, condition for a successful social benefit challenge. Conversely, the correlation indicates that a significant budgetary impact poses a serious, if not insurmountable, barrier to success. The social benefit equality cases have also been the occasion of the Court’s elaboration of a doctrinal framework for factoring budgetary impacts into the adjudication of rights claims under the Charter. Much of the discussion in this regard has been on the question of at what stage of Charter analysis — the rights analysis, the section 1 analysis (and its subparts), and/or the remedy stage — the public cost of rights recognition should or should not be weighed against other factors. From an initial position of high principle — that governmental concerns about saving time and money should not trump rights — the jurisprudence has, over the past 30 years, reached a point at which the financial impact on government may play a significant role at all three stages of analysis, often curtailing a meaningful exploration of both the Charter values and the non-monetary regulatory concerns at stake. If the Charter equality guarantee is truly to be given substantive content, the n judges, lawyers and legal scholars need to work out a more coherent framework for taking account of budgetary impacts. an overview is provided of the sorts of considerations that need to be integrated into such a framework.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| 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".