Calling power to account : law, reparations, and the Chinese Canadian head tax case
Bibliographic record
Abstract
Preface and Acknowledgments Contributors Context and History Mack v. Attorney General of Canada: Equality, History, and Reparation David Dyzenhaus and Mayo Moran Litigating Injustice Avvy Go Legal Discrimination against the Chinese in Canada: The Historical Framework Constance Backhouse Can We Do Wrong to Strangers? Audrey Macklin The Head Tax Case and the Rule of Law: The Historical Thread of Judicial Resistance to 'Legalized' Discrimination John McLaren Limits on Institutional Capacity to Address Injustice The Limits of Constitutionalism: Requiring Moral Behaviour from Government Mary Eberts Delivering the Goods and the Good: Repairing Moral Wrongs Catherine Lu Rights and Wrongs, Institutions and Time: Species of Historic Injustice and Their Modes of Redress Jeremy Webber Redress for Unjust State Action: An Equitable Approach to the Public/Private Distinction Lorne Sossin Legal Theory and Gross Statutory Injustice Gross Statutory Injustice and the Canadian Head Tax Case Julian Rivers The Juristic Force of Injustice David Dyzenhaus Private Right and Public Wrong The Timing of Injustice Lionel Smith Mack v. Attorney General of Canada and the Structure of the Action in Unjust Enrichment Dennis Klimchuk A Brief History of Mass Restitution Litigation in the United States Anthony J. Sebok Time, Place, and Values: Mack and the Influence of the Charter on Private Law Mayo Moran Appendix I: Appellants' Factum Appendix II: Mack v. Attorney General of Canada - Judgment of the Ontario Court of Appeal Index
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".