The Canadian Information Ecosystem
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,006 | 0,013 |
| Études des sciences et des technologies | 0,016 | 0,004 |
| Communication savante | 0,019 | 0,005 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,086 | 0,012 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».