Trust in the Age of Algorithms: How Gen Z Canadians navigate news, skepticism, and selective exposure
Bibliographic record
Abstract
La confiance dans le temps des algorithmes : Comment les canadiens de la génération Z dirigent les nouvelles, le scepticisme et l'exposition sélective RÉSUMÉ Facts & Frictions -Fall 2025 13 Faits & frictions -Automne 2025 La manière de consommer ainsi que la confiance dans les actualités évoluent d'une génération à l'autre, à mesure que les structures de production et de distribution des médias sont remodelées par la technologie.Au cours de la dernière décennie, l'essor des fils d'actualités sélectionnés par des algorithmes, des commentaires d'influenceurs et des cultures de l'information spécifiques à chaque plateforme a transformé la manière dont le public découvre et évalue l'information.Ce qui distingue la génération Z, ce n'est pas seulement son scepticisme, mais le fait qu'elle soit la première génération à avoir grandi en étant totalement immergée dans les réseaux sociaux, où coexistent actualités, divertissements et désinformation.Cette recherche analyse comment les Canadiens de la génération Z gèrent leur confiance dans cet environnement médiatique hybride.Grâce à une analyse qualitative, elle révèle un glissement de l'autorité institutionnelle vers la vérification participative et une confiance décentralisée.Le public de la génération Z recoupe les sources, privilégie les témoignages directs et choisit de s'engager ou de se désengager de l'actualité comme un acte d'autonomie.Ces résultats mettent en lumière une redéfinition de la confiance dans le journalisme numériquequi est fluide, situationnel et traité en temps réel.News consumption and trust are shifting across generations as the structures of media production and distribution are reshaped by technology.In the past decade, the rise of algorithmically curated feeds, influencer commentary, and platform-specific news cultures have transformed how audiences encounter and evaluate information.What makes Generation Z distinct is not simply their skepticism, but that they are the first generation to have grown up fully immersed in social media, where news, entertainment, and misinformation coexist.This research investigates how Gen Z Canadians navigate trust within this hybrid media environment.Through qualitative analysis, it reveals a shift away from institutional authority toward participatory verification and decentralized trust.Gen Z audiences cross-reference sources, privilege firsthand accounts, and selectively engage or disengage from news as an act of agency.These findings underscore a redefinition of trust in digital journalism-one that is fluid, situational, and negotiated in real time.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".