Canadian Intellectual Property Law: Cases and Materials, 3rd ed.
Bibliographic record
Abstract
Canadian Intellectual Property Law: Cases and Materials, 3rd Edition offers a comprehensive analysis of foundational concepts including copyright, patents, trademarks, industrial designs, passing off, and confidentiality. This casebook contains extracts from leading Canadian cases and IP legislation, paired with clarifying commentary and discussion questions. This approach allows students to test their comprehension and prepares them to engage in policy debates surrounding this important and evolving area of law. Chapters from the previous edition have been revised to reflect recent case law and regulatory changes. The third edition includes expanded discussion on the scope and coverage of IP protection in response to recent developments in the field of artificial intelligence and the inequitable distribution of COVID-19 vaccines. It also includes comprehensive coverage of Canada’s intellectual property law within the context of Indigenous legal traditions, addressing the need for inclusivity and reform. This collaborative text is a valuable teaching tool that bridges gaps found in similar texts by addressing the core areas of IP law within a single resource. [From Canadian Intellectual Property Law: Cases and Materials, 3rd Edition | Emond Publishing]
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.014 |
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".