2022 Year in Review and Considerations for 2023
Bibliographic record
Abstract
After almost six years as in-house counsel at the University of British Columbia, Michal returned to private practice where he continues to strengthen his ties to the post-secondary community in Canada as the Co-Chair of Clark Wilson’s Higher Learning Practice. Where there are new developments in the law, Michal is frequently one of the first to share his insights and one of the few who offers their time, expertise and strategic network to bring the higher learning community together and find an effective way forward. In this talk, Michal will review relevant copyright legislative amendments and case law from 2022 and discuss the issues that will keep the library community busy and engaged in 2023.
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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 | 0.024 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.028 | 0.011 |
| Insufficient payload (model declined to judge) | 0.112 | 0.096 |
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".