Book Review: Inclusive Equality: Relational Dimensions of Systemic Discrimination In Canada, by Colleen Sheppard
Bibliographic record
Abstract
IF YOU ASKED AN EQUALITY ACTIVIST or critical scholar to describe the Supreme Court of Canada's equality jurisprudence, the likely answer would be something along the lines of "bewildering, contradictory, fractured, and despair-inducing."There have been moments of relief-for example, an auspicious beginning in Andrews v Law Society of British Columbia 3 and a short-lived consensus with respect to the doctrinal formulation of equality in Law v Canada (Minister of Employment and Immigration).4 Overall, however, the equality case law provides a complicated and difficult legacy.In Inclusive Equality, Professor Colleen Sheppard does not set out to untangle or expose the doctrinal contradictions, although she does some of that along her way.Rather, her aim is optimistically creative: to articulate a new concept of equality-namely, inclusive equalitythat synthesizes the key insights of constitutional equality and statutory antidiscrimination jurisprudence, incorporating revised methodological approaches and new theoretical commitments.Moreover, she elaborates how her concept of inclusive equality can shift our gaze away from courtroom battles and can facilitate efforts to instill equality norms by means other than litigation.Most would agree that the endorsement in our Canadian Charter of Rights and Freedoms 5 jurisprudence of substantive rather than formal equality is one of
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".