Bibliographic record
Abstract
Regulation of communication infr astructures, resources, and dominant digital platforms is an increasingly pressing and provocative matter.Numerous government initiatives in Canada, the United States, and the European Union are currently deliberating various modes of regulation along several vectors, including: anti-trust law to break up the monopoly power of "big tech"; development of robust and actionable data privacy enforcement; content moderation to stem mis/ disinformation, hate speech, and extremism; artificial intelligence (AI) regulation and algorithmic accountability and transparency; ethical design principles for platforms; and the development of digital and data literacy programs.The Canadian Journal of Communication 's ( CJC 's) Policy Portal issued a Call for Papers in Fall 2021 seeking submissions to address these many facets of regulation from an interdisciplinary and intersectional approach.I am pleased to present the first of two Policy Portal articles on this theme.The next batch of articles will appear in CJC issue 48.2.Over all, the articles in this edition address an array of regulatory issues, including dimensions of platform regulation, Canadian broadcasting and telecommunication policy, broadband provision in rural and remote regions, and structures of participation in Canadian policymaking.The first series consists of four articles that address regulation in Canadian broadcasting, facial recognition technology, and children's digital games.
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.
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.025 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.028 | 0.011 |
| Insufficient payload (model declined to judge) | 0.389 | 0.225 |
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".