To Ensure that Justice is Done: Essays in Memory of Marc Rosenberg
Bibliographic record
Abstract
To Ensure that Justice is Done: Essays in Memory of Marc Rosenberg brings together leading scholars, practitioners, and jurists who have written chapters in tribute to the Honourable Marc Rosenberg’s legal and ethical contributions to the administration of justice. Inspired by his work as a teacher, a lawyer, and a judge, the contributors reflect on key trends and contemporary issues in jurisprudence, legal education, the administration of justice, and legal ethics. The contributors examine topics including wrongful convictions, social justice and the criminal law, the role of the judge and lawyer, challenges facing the law of evidence, the past and future of Charter justice, and the function of legal education in contributing to the administration of justice in Canada and abroad. The book is, thus, both a tribute to the life, work, and contributions of Marc Rosenberg, and an indispensable resource for all those concerned with the ways in which we seek justice in and through the law. [From To Ensure that Justice is Done: Essays in Memory of Marc Rosenberg | Thomson Reuters]
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".