Contested Sovereignties: States, Media Platforms, Peoples, and the Regulation of Media Content and Big Data in the Networked Society
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
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.
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 | 0.006 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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