Use of Copyright Content on the Internet: Considerations on Excludability and Collective Licensing
Bibliographic record
Abstract
The Internet has been a catalyst for problems latent within the copyright system. Fundamentally, the question is to determine under what circumstances should a copyright holder have a right to exclude others from using her copyright work on the Internet? This is the topic of this chapter. The underlying hypothesis is that policy analysis concerning copyright has shifted because it is now facing a number of formidable opponents, in most cases for the first time on that scale. Those opponents are other rights, including privacy. Copyright is not or no longer a closed system with exceptions looping back to a set of exclusive rights in which an appropriate equilibrium in the regulation of knowledge creation and dissemination was supposed to be reached. After an analysis of the problems that have emerged in trying to use copyright to exclude use on the Internet, the Chapter suggests possible solutions articulated along three types of use: those that should be free; those that should be licensed collectively (i.e., where the power to exclude is replaced with a remuneration system accompanied by standard conditions) and a small set of uses that can be licensed transactionally. In suggesting a greater role for collective (as opposed to individual) licensing, the paper considers the introduction of an Extended Repertoire System in Canada.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.061 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".