What are the implications if York loses?
Bibliographic record
Abstract
In July of 2017, many people in education were dismayed to learn that Access Copyright had won its lawsuit against York University. Yes, York has appealed and yes, both sides have the option to appeal one further time after the current appeal to the Supreme Court. But York’s loss has shaken faith in the fair dealing defence used so effectively in CCH and Alberta v. Access Copyright.\nWhat are the implications if York loses? Access Copyright would like a York loss to condemn all of the fair dealing policies developed by both post-secondary and K-12 education forcing education back into the tariff process. Does aYork loss condemn all of education to fair dealing overreach? Or does York have specific problems that do not broadly extend to other educational institutions? What would a final York loss mean to educational institutions across the rest of Canada? We will look at the current York decision and try to read the tea leaves for what might come next.\nRobert Tiessen is a Content Development Librarian within the University of Calgary Libraries & Cultural Resources. He has worked at the University of Calgary Library in various roles since 1999 after moving back to Canada from working as a librarian in Montana and Ohio. His interest was sparked in copyright after wondering why the copyright rules were so different between Canada and the US. He is a member of the CFLA Copyright Committee.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.078 | 0.013 |
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".