The Politics and Pedagogy of Young People's Digital Media Participation
Bibliographic record
Abstract
In this thesis I survey the terrain of digital interactions between youth, corporations and pop culture texts in order to complicate current visions of participatory culture. I argue that popular images of the empowered young users of a new digital democracy need to be complicated by asking questions about the politics of digital participation: about whose voices are heard, about where attention is centred, about how interactivity is defined, about who is rewarded for creative labour. The opening chapter introduces key issues within a critical examination of digital participation, including commodification, user agency and intellectual property. It also outlines my methodologies and my choice of research site – namely internet television, and the proliferation of corporate and youth practices around digitized television texts. The next two chapters provide case studies that identify and evaluate not only the interactions between corporate producers and young users, but also the power relations between the two. First, I analyze young women‘s video remixes of the program Gossip Girl. I consider the remixes as gendered texts that contribute new aesthetics and concerns, even as they reproduce dominant interpretations of contemporary girlhood. I also consider the distribution of the videos on YouTube, noting how their circulation simultaneously challenges corporate ownership and creates profit and promotion for those same corporate owners. Next, I examine interactions around the The Colbert Report. Focusing on the program‘s official discussion boards, I demonstrate how young fans have taken up Stephen Colbert‘s invitation to join in the parody by creating a vibrant, dialogic and rowdy community that has frequently come into conflict with Comedy Central producers. In their attempts to address these conflicts and create alternative spaces of their own, these young people gesture towards larger tensions over the control of public digital dialogue. The final chapter draws on my research and experience as a teacher to consider how these case studies might help us to frame our own educational projects. I call for a digital literacy curriculum that provides both a place for students to reflect on their daily activities within mediated environments and the opportunity to experiment with digital production.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".