IP Osgoode and the Intellectual Property Institute of Canada announce the winner of Canada’s IP Writing Challenge
Bibliographic record
Abstract
IP Osgoode and the Intellectual Property Institute of Canada (IPIC) are thrilled to announce the winners of the tenth annual edition of Canada’s IP Writing Challenge: In the Law Student category, Alyssa Gaffen, won for her entry, “Refining The Saccharin Doctrine: A Call for Clarity and Predictability in Canada’s Extraterritorial Patent Law”. In the Graduate Student category there were no entries. In the Professional category, the judges did not select a winner for this year’s Challenge. \nThe winner will be receiving a prize of $1,000 and, in addition to having her winning article showcased here on the IPilogue, the article will be considered for publication in the Canadian Intellectual Property Review (CIPR) or the Intellectual Property Journal (IPJ). We would like to thank our esteemed intellectual property experts who served as judges for the Challenge:\nThe Honourable Roger T. Hughes QC Professor Ikechi Mgbeoji Daniel R. Bereskin, QC\nWe look forward to next year’s IP Writing Challenge and continuing to help ignite a more vibrant public policy discussion on all facets of intellectual property law and technology.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.023 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.057 | 0.018 |
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".