Broadcasting Self-Injury for Change: Exploring the Presentation of Advocacy Strategies on YouTube
Bibliographic record
Abstract
This study employed an observational design to explore the presentation of advocacy strategies in YouTube videos orientated around the event of Self-Injury Awareness Day (SIAD).While previous research has focused on highlighting potential dangers of social media platforms like YouTube we approached our exploration from a perspective of advocacy to try and establish a more balanced understanding of how YouTube can, and does, contribute to self-injury discourses.The aim was not to define advocacy in YouTube videos but rather to understand more about the ways advocacy strategies might be presented, and what this looks like in the content produced by individuals with lived experience of self-injury.We expected to find content concerned with improving the lives of people experiencing self-injury, which could be interpreted as advocacy.Thematic analysis of thirty videos offered four themes: Community, comradery, and cheerleading; Education around self-injury; Authentic representation of self-injury; Development of advocacy identity.Findings indicate advocacy through YouTube exists and results in supportive, passionate, and resourceful content with potential to improve the wellbeing of individuals who self-injure, countering narratives of self-injury related content having a solely negative impact.Further research is required to fully understand, and harness, the potential of advocacy through online mediums.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".