Trust in the Age of Algorithms: How Gen Z Canadians navigate news, skepticism, and selective exposure
Bibliographic record
Abstract
les nouvelles, le scepticisme et l'exposition slective RSUM Facts & Frictions -Fall 2025 13 Faits & frictions -Automne 2025 La manire de consommer ainsi que la confiance dans les actualits voluent d'une gnration l'autre, mesure que les structures de production et de distribution des mdias sont remodeles par la technologie.Au cours de la dernire dcennie, l'essor des fils d'actualits slectionns par des algorithmes, des commentaires d'influenceurs et des cultures de l'information spcifiques chaque plateforme a transform la manire dont le public dcouvre et value l'information.Ce qui distingue la gnration Z, ce n'est pas seulement son scepticisme, mais le fait qu'elle soit la premire gnration avoir grandi en tant totalement immerge dans les rseaux sociaux, o coexistent actualits, divertissements et dsinformation.Cette recherche analyse comment les Canadiens de la gnration Z grent leur confiance dans cet environnement mdiatique hybride.Grce une analyse qualitative, elle rvle un glissement de l'autorit institutionnelle vers la vrification participative et une confiance dcentralise.Le public de la gnration Z recoupe les sources, privilgie les tmoignages directs et choisit de s'engager ou de se dsengager de l'actualit comme un acte d'autonomie.Ces rsultats mettent en lumire une redfinition de la confiance dans le journalisme numriquequi est fluide, situationnel et trait en temps rel.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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".