Bibliographic record
Abstract
At its core this myth embodies the Trudeauian ideal of Canadian society - one that features a constitution that empowers impartial judges at the expense of politically motivated legislators; one that allows each individual to enjoy a uniform range of rights, freedoms, and means of belonging to the larger Canadian society; and one that seeks to ensure the primacy of the national government rather than the provincial. Trudeau called his vision the Just Society. But justice is an illusive and amorphous concept. Defining it, much less institutionalizing it, is fraught with risk. In modern liberal democracies, justice is typically understood as the product of some mix of liberty and equality, process and substance, with the amount of each component varying according to taste. It is not unusual for political actors to seek to institutionalize their own formulas for justice, but it is also not reasonable to expect these formulas to go unchallenged. Such a challenge represents the dominant theme of this volume. Contributors include Donald E. Abelson, Tom Flanagan (University of Calgary), Patrick James, James B. Kelly (Brock University), Michael Lusztig, Christopher P. Manfredi (McGill University), Hudson Meadwell (McGill University), Anthony A. Peacock (Utah State University), Mark Rush (Washington and Lee University), and Shannon I. Smithey (Kent State University).
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.073 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".