The policy battle over information and digital policy regulation: a canadian perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.032 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it