Bibliographic record
Abstract
Copyright policy, like other major areas of public policy, requires a solid anchoring in fundamental principles. The perceived need to anchor copyright debates in a solid policy context and, hence, to develop a coherent (and hopefully convincing) narrative has been the subject of excellent contemporary research. We are indebted to a number of scholars for their work in this area. The attempt to find normative applications from a historically derived model for copyright is not either. However, the research thus far tends to provide a blurred picture, by espousing justiflcatory theories based on one or many of the following: commercial and personal Interests of authors, understood as property and/or liability rules; commercial interests of publishers and other “rights holders”; and/or the social costs of overprotection and the related economic‐driven search for an optimal point of protection. This article looks at pieces in the Canadian narrative puzzle and tries to present a faithful picture of its current stage of evolution. To do so, however, a detour via England is required, because that is whence the soil from Which the Canadian narrative comes. This historical detour will be the focus of Part 1. Part III will suggest a path for the next stages of the Canadian narrative that is both consistent with international norms and hopefully useful in moving the debate forward. The part ends with a brief look at the impact that the linkage with trade rules may have on copyright.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".