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Record W4403215771 · doi:10.1177/13582291241291024

Confronting our failures: Tensions in remedying systemic discrimination in Canada

2024· article· en· W4403215771 on OpenAlexaffabout
Sophie Bisping

Bibliographic record

VenueInternational Journal of Discrimination and the Law · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiscrimination and Equality Law
Canadian institutionsMcGill University
Fundersnot available
KeywordsPolitical scienceLawSystemic riskLaw and economicsSociologyEconomics

Abstract

fetched live from OpenAlex

This article examines the complexities of providing remedies for systemic discrimination in light of evolving understandings of equality and justice. Despite constitutional and statutory protections affirming the right to live free from discrimination, there remains a significant gap between recognizing systemic discrimination and implementing effective remedies. The 2021 case of Canada (Attorney General) v. First Nations Child and Family Caring Society of Canada serves as a focal point to highlight the shortcomings of the current remedial framework, which often prioritizes corrective over transformative justice, reflecting a formal rather than substantive approach to equality. In this case, the Canadian Human Rights Tribunal found that Canada’s inequitable funding of First Nations child welfare services was discriminatory on the grounds of race and national or ethnic origin. It ordered Canada to compensate every child impacted, while simultaneously imposing systemic remedies such as reforming federal funding policies. By exploring the Tribunal’s decision to uphold both individual and systemic remedies, the paper argues for a more integrated approach that moves beyond the dichotomy of individual versus systemic discrimination. It advocates for co-designed remedies informed by the perspectives of affected communities, calling for a shift in how systemic discrimination is addressed within Canada’s legal framework.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.336
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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