Competence Concerns in Charter Adjudication: Countering the Anti-Poverty Incompetence Argument
Bibliographic record
Abstract
Canadian courts are reluctant to impose anti-poverty obligations upon governments under the Canadian Charter of Rights and Freedoms. Concerns over the limits of the institutional competence of courts have played an explicit role in this reluctance. The anti-poverty incompetence argument that has thus emerged is an instance of a broader concern over competence that is evident across the spectrum of types of Charter cases. This article traces the emergence of a judicial framework for recognizing and responding to competence concerns in early Charter adjudication and describes the main lines of its evolution in subsequent cases. At the same time, and for the most part remaining within the confines of issues and arguments contained in accumulated Charter case law, the article critically evaluates the ongoing application of the framework in anti-poverty Charter cases. The central argument of the article is that the case law on competence concerns cannot justify placing relatively greater limits on the availability or rigour of Charter protection for anti-poverty claims than for other types of claims. Indeed, the argument is that the case law in fact offers encouragement to courts to pursue responses that manage the concerns or improve competence, thereby allowing equally fulsome protection for anti-poverty
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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 | 0.039 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.052 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.016 | 0.015 |
| 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".