The Canadian Information Ecosystem
Bibliographic record
Abstract
Democratic governments have been seized with a concern for mis- and disinformation. There is a malaise that we live in an accelerating post-truth era where a foundational pillar of democracy - the free exchange of factually accurate information - is endangered. And there is a strong feeling something must be done. In this report, we assess this concern. We ask: what claims are being made about the nature of the information ecosystem? Can we evaluate them? How are the attitudes of Canadians changing? Are the digital media we produce and consume harming or helping? In some cases we are able to provide straight-forward answers, in others we are able to evaluate some claims, and still in others we can simply describe what we can know today and lay a path forward for future research.To do all this, we employ survey and digital trace data. Using large national samples dating five years apart as well as targeted samples during key political moments (by-elections and extreme weather events), we are uniquely able to speak to trends and to how events may shape behaviours and attitudes. Using a novel digital trace data collection method that links Canadian political influencers across their information ecosystem footprint, we are also uniquely able to comment on concentration and fragmentation in the Canadian information ecosystem. The report details numerous findings. The four most central are:First, we find that most Canadians are inattentive to politics. Canadians do not regularly consume political news, generally have low levels of political knowledge, and have low awareness of important political figures in Canada and the United States. When news and political information were removed from Facebook, Canadians (including politically active ones) did not noticeably change their behaviour. Second, with the important caveat of inattentiveness, we find that in the aggregate individuals' news consumption and attitudes have been generally stable in the last five years. We observe remarkably few shifts in what and how people consume their news. Despite this, we do find a significant decline in media trust over the last five years. We do see an increase in use of social media for news, with a rapid rise of TiKTok as well as an increased use of Instagram, WhatsApp, Reddit, and SnapChat. Those who use social media for news tend to be less trusting of traditional media.Third, we find a high degree of concentration of influence in digital media. Social media provides unequal opportunities to be heard and to have an impact on the conversation. Politician impact in particular is highly unequal. Several large Canadian news outlets, notably Global News and CTV have been able to amass large social media followings. Fourth, we find that the online discourse among political influencers is not highly segregated in the typical fashion. Instead, the federalism of Canada is important, with politicians tending to share similar content as their provincial political community. Party affiliation does not structure the entire information ecosystem. Certain topics of discussion do tend to be associated with some political party families more than others, with left parties tending to focus on health than any other single topic, while the rest of the political spectrum gave comparatively more emphasis to international issues as well as those of government and governing.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.012 |
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".