Bibliographic record
Abstract
This is the second issue in a two-part series of the Policy Portal on the theme of regulation.In the first issue, 48.1, four articles addressed an array of regulatory issues including current initiatives in Canadian broadcasting and telecommunication policy, privacy and facial recognition technologies, and children's digital games.This second issue consists of five articles that discuss regulation in Canadian telecommunications, including broadband and wireless spectrum, competition policy in mergers and acquisitions, antitrust options for digital platforms, and the politics and governance of pornography platforms.Woven throughout these articles are considerations of structures of participation in policymaking and modes of public engagement-how best to effectuate meaningful participation and the maintenance and nurturance of the public interest in telecommunications and digital platforms.Through analysis of numerous Canadian Radio-television and Telecommunications Commission (CRTC) proceedings and decisions on the deployment of rural and remote broadband, including, notably, submissions from Indigenous organizations, Heather E. Hudson, Rob McMahon, and Bill Murdoch, in "Beyond Funding: Barriers to Extending Broadband in the Indigenous North," advance policy proposals toward ameliorating barriers.Barriers include wholesale access to transport services and existing support structures, such as poles and towers, access to rights-of-way that are jurisdictionally complex and may include Indigenous reserves and territories, appropriate Shade, Leslie Regan.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.038 | 0.020 |
| Insufficient payload (model declined to judge) | 0.221 | 0.145 |
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".