“But how could anyone rationalize policies that discriminate?: Understanding Canada’s Failure to Implement Jordan’s Principle
Bibliographic record
Abstract
This article seeks to understand Canada’s failure to implement Jordan’s Principle, a child-first policy ensuring First Nations have access to the same level and quality of services available to other children. Policy-making in Canada rests firmly within a neoliberal political framework that extends market-based thinking to all aspects of social life. Neoliberal thought interlocks with stories of Other to inform notions of deservingness as well as one’s potential as a valuable citizen with something to contribute. Social policy decisions, including the decision to implement a particular policy or not, offer a means through which to disseminate neoliberal values and norms. As self-determining peoples with distinct rights, lands, and governance structures, First Nations transgress the image of the “good” neoliberal citizen in a variety of ways. Neoliberalism holds that punitive measures are sometimes needed to encourage citizens to adopt particular norms, and this allows policy makers to rationalize and justify policies that discriminate against First Nations children. Stereotypes about Indigenous peoples are also used to manipulate public sentiment in favour of government policy. Canada’s failure to implement Jordan’s Principle can be understood as part of a broader strategy to encourage First Nations to rescind their distinct rights and assimilate as good neoliberal citizens.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".