Gen Z Digital Media Usage and Trust
Bibliographic record
Abstract
In modern times, the issue of media trust and its impact on people’s media use has taken on increased importance. The degree to which people trust the news media and how much it relates to their use of different types of media is not apparent, particularly among members of Gen Z. This research examines news media trust and its influence on media use on Gen Z. Specifically, this research examines media and information consumption among Gen Z for gathering political news and their level of trust among various digital channels. This was accomplished via an online survey of Gen Z college-aged students (N=99) enrolled at a private university in the northeastern United States. The longitudinal study was administered during two academic semesters, one during fall 2023 and the other in fall 2024 during the presidential election. The empirical findings supported the hypothesis that there is a significant difference between the level of media trust and political ideology. However, there was no significant difference in the average media trust value between Republicans and Democrats The study also found that as a person’s media trust increased so did their weekly usage of various social media networks. Specifically, the findings also indicated that Facebook, Twitter, Pinterest, Snapchat, and TikTok usage frequency had a positive correlation with media trust
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".