Bibliographic record
Abstract
Generation Z (Gen Z), also named digital natives, is the first to have been born after the mass-adoption of the Internet, especially the social media.Through all kinds of international digital platforms, Gen Z has more access to a vast number of diverse information than previous generations.Interconnected on world media platforms, Gen Z has become a generation doing Internet-based communication from a young age.Exposed to global communication platforms, especially global social media platforms, such as Twitter, Facebook, Instagram, YouTube, TikTok etc., how Gen Z's perceptions and attitudes are shaped by online content from all over the world is important to study.However, either studies about Gen Z's online media use and their perceptions of another country or comparative studies across countries are scarce.Furthermore, studies focus on Gen Z's media use in a global context, especially news consumption, is of vital importance to the understanding of the world.Our journal, OMGC, made some efforts this year to fill this gap.We organized a preconference at the 2023 Annual conference of the International Communication Association (ICA) in Toronto, Canada.In his keynote speech of our preconference titled, "Zoomers, Millennials, Gen X and Boomers?The News Finds Me Perception as a Media Effect Equalizer and Implications on Global Communication," Homero Gil de Zuniga proposed a model of media use of young people.He argued that in a social media age, Generation Z, instead of searching for news, rely on "news finds me".In addition, two panels, "Media Use and Gen Z's World View" and "Children and News: Lessons Learnt and Future Directions" as well as 14 papers with topics such as the effects of social media use and international news on Gen Z's world view, politics and Gen Z's media use, artificial intelligence and Gen Z, digital activism and Gen Z, and cross-generational comparisons were presented at the preconference.See our full preconference program at https://omgc.shisu.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".