The policy battle over information and digital policy regulation: a canadian perspective
Bibliographic record
Abstract
Abstract Many countries find their information and digital policies still dominated by traditional stakeholders, particularly the content industry, major telecom companies, and marketing groups, yet Canada has experienced a notable shift in perspective with a strong and influential public interest voice. This shift toward public interest and participation in the development of Canadian information and digital policies has led to legislation, regulation, and policy outcomes that once seemed highly unlikely. This Article seeks to better understand the changing role of the public in Canadian information and digital policymaking by framing the developments as an ongoing policy development process featuring a series of closely linked changes and responses. The emergence of public participation on information and digital policy issues occurred across a spectrum of issues, yet the traits were strikingly similar: grassroots efforts reliant on social media and the Internet to capture media and public attention and focus it on consumer perspectives, minimal interest from government and regulators; and initial dismissal giving way to hostility from incumbent stakeholders. The Article identifies some of the reasons behind the shift, including the growing importance of information and digital policies, the impact of digital advocacy tools, and the shifting policy pyramid in which users have now largely leapfrogged corporate interests as policy influencers. While the shift does not mean the public interest wins on every issue, it does suggest an important change in influence with long-term ramifications for the development of information and digital policy in Canada that others may seek to emulate.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.047 | 0.045 |
| Scholarly communication | 0.031 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".