Introduction to Critical Conversation in Canadian Public Law
Bibliographic record
Abstract
The introductory chapter to Critical Conversations in Canadian Public Law situates the book "in the midst of some of the most significant social, economic, and political struggles of the past decade", from the COVID-19 pandemic to the Gaza genocide. The introduction describes how the book "seeks to reflect and ignite critical conversations about the centrality of public law and its institutions, broadly defined and deeply contested, to the (re)production of current inequities." It outlines two ways in which the collection is "critical": first, the critical legal methods employed by the contributors (e.g., acknowledging law's political operation, understanding law's relationship with power, and looking beyond descriptive accounts of law to consider its materiality and normativity); and second, "in terms of the importance, urgency, and necessity of deepening our understandings of the relationship between public law and contemporary inequities." Finally, the introduction identifies "five cascading themes reflected across the chapters in this collection—and across our varied experiences with the law—that are pivotal to the law's consistent mobilization to reify extant power disparities in society [...] exceptionalism, capitalism, segmentation, incrementalism, and formalism."
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.030 | 0.018 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".