Examining Critical Approaches to Social Media Education and Research in Canada
Bibliographic record
Abstract
This thesis explores the conditions, limits and possibilities for advancing social media education and research in Canada. Drawing from LeGreco and Tracy’s (2009) discourse tracing approach, I analyzed connections between evolving views, discourses and practices relating to social media across two transdisciplinary fields: media education and social media research. I conducted interviews with experts from Canadian media education organizations working at the crossroads of research, education, and community settings. I then completed a narrative review of media education and social media research from the last 15 years. The findings reveal multifaceted challenges of accessibility relating to social media education, encompassing both systemic inequalities and epistemological limitations in knowledge production and dissemination. In response to these challenges, this thesis highlights promising methodological and pedagogical approaches to social media education and research that promote creative, critical and community-driven ways to make sense of and engage with social media.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.040 | 0.017 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".