Bibliographic record
Abstract
Given the illustrious career of Isaac Pitblado and the more than 50 year history of this lecture series, it is truly an honour to be invited to give one of this year's keynote lectures.I am grateful to the Law Society of Manitoba, the University of Manitoba, Faculty of Law, the Manitoba Bar Association, the Isaac Pitblado Lectures Selection Committee and Jennifer Schulz for the generous invitation to give this lecture as part of this year's Isaac Pitblado Lectures.Hilary Fender provided excellent research assistance and Tracy Lloyd provided very helpful administrative assistance.My approach to this lecture has been significantly influenced by our mandatory first year Osgoode Hall Law School course entitled: "Ethical Lawyering in a Global Community".We started that course six years ago as part of a major reform to our first year curriculum.3 And although I was the inaugural director, and continue to be the director, for that course, many people -including former co-instructors, graduate teaching assistants, students and others -have influenced not only the course outline and materials, but also my thinking about the issues that we cover.4
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.034 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 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".