New Osgoode podcast called “4 Questions For” launches today
Bibliographic record
Abstract
Osgoode Hall Law School launched a new podcast on October 1 called “4 Questions For” featuring great conversations about legal education, the profession and the law. The podcast will be updated with new content on a monthly basis throughout the academic year.\nIn Episode 1, Osgoode Alumni Association Board co-president Patricia Olasker ’77, a partner at Davies Ward Phillips and Vineberg LLP, will have four questions for Dean Mary Condon about the challenges and opportunities in legal education today.\nUpcoming episodes this fall will include Nye Thomas, Executive Director of the Law Commission of Ontario, speaking with Carole Piovesan ’09 of INQ Data Law in November about artificial intelligence – AI – and its ramifications for lawyers and the practice of law.\nIn December, Osgoode Professor Emeritus Harry Arthurs will chat with Professor Philip Girard about Arthurs’ memoir, Connecting the Dots – The Life of an Academic Lawyer.\nTo download “4 Questions For” podcasts, visit: soundcloud.com/osgoode_yorku
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.673 | 0.286 |
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".