Contested Sovereignties: States, Media Platforms, Peoples, and the Regulation of Media Content and Big Data in the Networked Society
Bibliographic record
Abstract
This article examines the legal and normative foundations of media content regulation in the borderless networked society. We explore the extent to which internet undertakings should be subject to state regulation, in light of Canada’s ongoing debates and legislative reform. We bring a cross-disciplinary perspective (from the subject fields of law; communications studies, in particular McLuhan’s now classic probes; international relations; and technology studies) to enable both policy and language analysis. We apply the concept of sovereignty to states (national cultural and digital sovereignty), media platforms (transnational sovereignty), and citizens (autonomy and personal data sovereignty) to examine the competing dynamics and interests that need to be considered and mediated. While there is growing awareness of the tensions between state and transnational media platform powers, the relationship between media content regulation and the collection of viewers’ personal data is relatively less explored. We analyse how future media content regulation needs to fully account for personal data extraction practices by transnational platforms and other media content undertakings. We posit national cultural sovereignty—a constant unfinished process and framework connecting the local to the global—as the enduring force and justification of media content regulation in Canada. The exercise of state sovereignty may be applied not so much to secure strict territorial borders and centralized power over citizens but to act as a mediating power to promote and protect citizens’ individual and collective interests, locally and globally.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.086 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".