By Young Adults, for Young Adults: A Participatory Approach to Co-Designing Social Media Strategies for Knowledge Mobilization and Engagement
Bibliographic record
Abstract
Social media is increasingly recognized as a powerful tool for knowledge mobilization, activism, and advocacy—particularly for young adults who are often excluded from institutional research and decision-making. However, these platforms are not neutral; algorithmic biases, corporate interests, and structural barriers shape whose knowledge is amplified and whose is suppressed. This study examines the nuances of using social media for participatory, youth-led knowledge mobilization through the Valuing Opinions and Inspiring Change through Engagement (VOICE) Study, a youth-designed and youth-led initiative exploring social media engagement strategies to reach equity-deserving and hardly reached young adults in mental health advocacy. Using an iterative, co-created approach, the study employed both qualitative and quantitative analyses to examine engagement with different types of social media content. The findings reveal tensions between visibility and meaningful interaction: while reels expanded audience reach, they did not consistently foster engagement. Medium-relevance posts addressing relatable mental health topics sustained engagement, highlighting the need to balance reach with substantive impact. The study also considers digital inequities, as youth without stable internet access or those avoiding social media for privacy and well-being concerns remained excluded. Through a participatory and reflexive framework, this study challenges assumptions that social media inherently democratizes knowledge. It underscores the need for multi-modal strategies integrating online and offline engagement and calls for critical examination of corporate social media’s structural limitations in activist research.
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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 | 0.051 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".