How are civic cultures achieved through youth social-change-oriented vlogging? A multimodal case study
Bibliographic record
Abstract
Debate over conceptual definitions is prominent within the body of literature dealing with emerging patterns of civic engagement and political participation among youth information and communication technology–enabled politics. This article contends that advancing new knowledge in this field is also dependent upon fine-grained empirical analysis of digital traces of youth participation. Drawing on a close analysis of two youth-produced vlogs, we show that adolescents’ commitment to social change can be creatively achieved through video making. Informed by a socio-semiotic approach to multimodal analysis and by Peter Dahlgren’s concept of online civic cultures, our qualitative analysis highlights two main patterns we found in young people’s vlogs aimed at raising awareness about social issues. First, we found that to impact their intended audiences, vloggers presented themselves as creative choice makers and as savvy insiders of youth civic cultures on YouTube. Second, we found that vloggers successfully managed the risk of being the target of online hostility using rhetorical devices and tactics that smoothed counterpositions. Overall, our multimodal case study shows that contrary to traditional approaches to successful communication based on textual coherence, a mix of consistency, disruption, and contradiction can be used purposefully in public speech in order to manage difficult, risky topics. As we demonstrate that visual-based communication on social network sites such as vlogs posted on YouTube is not neat and tidy, we illuminate the vloggers’ shifting identities, opinions, and concerns. This evidence-based observation calls for more in-depth small case qualitative analyses for investigating the multiple affordances of civic talk online and its democratic potential. This article contributes to the ongoing conceptual redefinition of youth civic engagement and political participation in the face of fast-evolving sociotechnical change.
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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 | 0.016 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".