Youth, Media Activism, and Communication Counterpower: A Comparative Study
Bibliographic record
Abstract
Around the world, young people are leveraging new media to engage with civic and political issues outside the confines of traditional public institutions (e.g. voting). This shift seemed initially to signal enormous potential for democratic renewal globally, with the emergence of new political actors and new forms of political engagement. The Arab Spring, the Occupy movement, Black Lives Matter, March for Our Lives, and the Global Climate Strikes offer examples of the creative ways in which young people (and others) can use new media to share information, connect with peers, mobilize with goal of advancing their causes. But following the resurgence of authoritarian leadership in the Middle East and recent political events such as the 2016 U.S. election and the #Brexit campaigns, intense debates have arisen about whether social media use in fact is as likely to undermine as to advance democratic processes. Social media use presents new opportunities for youth who were previously excluded from formal channels of political participation; but increasingly, these platforms subject youth to surveillance, censorship, and other forms of repression. In this project, I examine how politically active young people in democratic and non-democratic countries leverage social media to exercise voice in contentious politics. My dissertation draws on in-depth interviews, social media walkthroughs, and surveys with 91 young media activists (ages 18-30) in two democracies (Canada and the US) and an authoritarian regime (Cambodia). In this dissertation, I develop conceptual and analytical tools to center the voices of youth—especially marginalized youth—that have been obscured by the traditional focus on public, institutionalized forms of participation. This research aims to contribute to emerging scholarship on youth and media activism, and to inform the development of initiatives that encourage young people’s constructive participation in public spheres.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".