How is Canadian media's platform dependence evolving? A closer look at the online news act's effects
Bibliographic record
Abstract
From a perspective inspired by the political economy of communication and platform studies, this paper examines how the power relations between platforms and news media have changed in Canada since the adoption of the online news act. We will particularly focused on the concept of platform dependency: according to Poell, Nieborg and Duffy (2021), digital producers are platform-dependent when they rely on platforms to create, distribute, market and monetize their content and services. In the months following the bill's adoption, Canadian and Quebec journalistic organizations denounced Meta and its contestation of the bill in their public speeches. According to recent studies, these actions convinced a third of Canadians to change their information habits, notably by returning to mainstream media websites (Brin and Charlton, 2024). On the other hand, several researchers, including Dwayne Winseck (2022), also criticized the regulators for their lack of vision in the very creation of the bill, which did not tackle the root of the problem in their view (monopolistic control of advertising market, cross-market consolidation, personal data harvesting). But while we know a lot about how the journalistic profession and experts reacted, we still know very little about how platforms adapted to Canadian regulations.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".