Reflections on You Tube as a Site for Civic Engagement and Learning
Bibliographic record
Abstract
This paper is based on a public lecture held at Soka University in Tokyo, on June 6 th, 2018. It explores some of the literature and my insights around young people’s usage of social media platforms and YouTube specifically for civic engagement and learning. First, I address the prevalence of social media in Canada and Japan, and explore some concerns that arise around the relationships between technology and youth. I introduce concepts like civic engagement and participatory cultures to examine the relationships between their consumption and production of social media texts- on YouTube particularly- with online political participation and learning. I end this paper with my thoughts on the importance of YouTube media education for critical pedagogy. This paper aims to motivate educators to recognize the important political and educational role that YouTube plays in the lives of modern youth, and to critically explore this platform with their students.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".