User Rights in Canadian Copyright Law
Bibliographic record
Abstract
It is very kind of you to invite me to talk about User Rights at the Association’s Copyright Symposium. In ancient times, symposia were occasions to discuss and debate matters of great moment, to the accompaniment of copious food, wine, and revelry. Zoom of course limits the conduct of this symposium but I hope participants will take the ancient precedent to heart at their end of their internet connection. Sometimes a symposium would wait until the hunt for a wild boar was over before starting. I hope that was not the motive behind your organizers hunting me down for this talk. I shall try to avoid being the bore of your event.\nOver today and tomorrow you will discuss how user rights do and should work. I’ll try to set the stage by talking about where these rights now stand in Canada and how they got there. I’ll concentrate on fair dealing but first I’ll say a few words about the right to use unsubstantial parts of a work. This is not usually thought of as a user right, but it can be viewed that way, and it can be as important as fair dealing.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.032 | 0.019 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".