Mobilizing Transdisciplinarity to Address the Good Versus Bad Dichotomy: Thinking Critically About Current and Future Youth Social Media, Peer Relationships, and Mental Health Research
Bibliographic record
Abstract
North American rates of adolescent social media use hover between 98 and 100%, with 45% of adolescents reporting being online “almost constantly”. Despite its prevalence, social media use is also controversial. If social media is now woven into the fabric of social interactions, what are the mental health implications for youth growing up in “a digital age”? This paper discusses the potential applications of transdisciplinarity by considering the question in three ways: first, the polarized research is presented, suggesting that social media has the power to either positively or negatively direct youths’ social and psychological trajectories; second, the dichotomy is challenged; and third, transdisciplinary applications are considered. As a complex, novel, and nuanced topic, the study of social media, peer relationships, and mental health demands a paradigm that is able to accommodate complexity, nuance, and novelty in a critical, reflexive and meaningful way. Transdisciplinarity presents scholars an opportunity to tackle this challenge. This paper discusses the prevailing research surrounding social media, peer relationships, and mental health to challenge the good/bad binary and lay the foundation for approaching this topic from a transdisciplinary lens in future 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 | 0.082 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.012 | 0.083 |
| Scholarly communication | 0.027 | 0.039 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".