Figuring Digital Cascades: Issue Framing in Digital Media Ecosystems
Bibliographic record
Abstract
On November 17, 2015, the newly elected Canadian government led by Justin Trudeau made an announcement that became a turning point in the heated debate around the plan to build the Memorial to the Victims of Communism in Ottawa. The government’s decision to scale the project down was massively republished and generated a heavy stream of 2,055 publications and interactions. The virality of such phenomena is sometimes described in the literature as an “information cascade” characterized by a complex and expanding series of media content that is republished, shared, and commented upon in digital public spheres, reaching a growing number of people. Our research aim is twofold. From a theoretical point of view, we combine Entman’s cascade model with the perspective of platform studies. From an empirical point of view, we put this model to the test through a case study of the cascading data flows that emerged during this public debate. We found three key factors that constituted and shaped this information cascade: 1) the economic structure of the Canadian media market, and especially the concentration of media ownership, which is notably high in the Canadian media ecosystem; 2) data-exchange mechanisms and algorithmic filtering that drive the process of news aggregation, quickly spreading media content without being a significant source of user engagement; 3) grassroots engagement in diasporic media, which activates micro public spheres around nested interests and political standpoints regarding the public issue.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".