Young People, Digital Media, and Engagement: A Meta-Analysis of Research
Bibliographic record
Abstract
New technologies raise fears in public discourse. In terms of digital media use and youth, the advice has been to monitor and limit access to minimize the negative impacts. However, this advice would also limit the positive impacts of digital media. One such positive impact is increased engagement in civic and political life. This article uses meta-analysis techniques to summarize the findings from 106 survey-based studies (965 coefficients) about youth, digital media use, and engagement in civic and political life. In this body of research, there is little evidence to suggest that digital media use is having dire impacts on youth’s engagement. We find that the positive impacts depend on directly political uses of digital media, such as blogging, reading online news, and online political discussion. These online activities have off-line consequences on participation, such as contacting officials, talking politics, volunteering, and protesting. We also find a very strong relationship between online political activities, such as joining political groups and signing petitions, with off-line political activities, which undermine claims of slacktivism among youth. Finally, while research generally assumes a causal flow from digital media to participation, the evidence for the alternative causal flow is strong and has very different implications on interventions designed to address youth’s levels of engagement in civic and political life.
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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 | 0.042 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".