Supporting Alternative Incentive Mechanisms for Digital Content: A Comparison of Canadian and US Policy
Bibliographic record
Abstract
This paper compares the Government of Canada’s copyright focused approach for encouraging the production of digital content with the U.S. Government’s adoption of a range of incentive systems for the production of content through a content analysis of government policy papers. The first part of the paper examines Canadian policy outlined in the Improving Canada’s Digital Advantage consultation paper and the proposed amendments to the Copyright Act (Bill C-32). The paper argues the government is overly reliant on copyright to encourage the production of creative digital content. Though Bill C-32 would expand the definition of fair dealing and create a user generated content exception, the effectiveness of these measures is severely limited by through the proposed protections for technological protection measures. The second part of the paper examines innovative alternatives to copyright that are being promoted by the U.S. government. The Obama Administration’s Open Government Directive not only provides citizens with access to government data, but also calls on federal departments to use prizes to encourage innovative uses of the data. The U.S. National Institutes of Health has taken a leading role in promoting open access publication of research funded with federal monies by requiring deposit of publications resulting from research in the open access repository PubMed Central. The paper concludes by positing that Canada’s digital economy strategy would be strengthened by providing greater federal support for alternatives to intellectual property such as open data and open access and lessening the focus on copyright as an incentive digital content production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".