Language and Discourse in the Canadian Copyright Act Review
Bibliographic record
Abstract
This study has examined the 2017-2019 Parliamentary review of Canada’s Copyright Act by the Industry, Science, and Technology (INDU) Committee, focusing on the impact the discourse around copyright in Canada has on legislative change. We have investigated the recommendations for amendments to the Copyright Act, and the rationales put forward to support them, made to the INDU Committee by various types of stakeholders, as well as the interactions between stakeholders and committee members in their meetings. We aimed to make connections between these contributions and the committee’s own resulting report and recommendations, as well as with any responses or actions taken by the federal government. Our primary focus has been on areas of discussion and debate relevant to higher education, including fair dealing, collective licensing in Canada, Indigenous rights, Crown copyright, technological protection measures, and contract override of user rights. We have a particular interest in using our findings to support future advocacy for copyright and user rights in higher education and libraries. See the Wiki for our publications and presentations. A number of our data files and codebooks are available below. Co-Investigators: Jennifer Zerkee, Copyright Specialist, Simon Fraser University; Stephanie Savage, Scholarly Communications and Copyright Services Librarian, University of British Columbia Research Assistants: Arianna Alcaraz (University of Alberta School of Library and Information Studies) 2023-2024; Will Power-Jenkins (University of Toronto iSchool) 2022-2023; Jentry Campbell (UBC iSchool) 2020-2021; Jessi Robinson (UBC iSchool) 2021 This project has received funding from an SFU/SSHRC Small Explore Grant (2022) and a CARL Research in Librarianship Grant (2020).
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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 | 0.109 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.071 | 0.059 |
| Scholarly communication | 0.042 | 0.011 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".