Digital Media, Mental Health, and Transformative Learning: Conceptualizing Impacts on Marginalization and Equity
Bibliographic record
Abstract
This article describes the results of a qualitative pilot study, conducted with individuals facing multiple statuses of marginalisation and self-reported barriers to formal education (n = 8). This study emphasises the potential utility of fictional media based on narrative or storytelling pedagogies, and posits that the increasing use of arts-based methodologies and media for knowledge translation can improve agency and access to information. With the objective of understanding how digital media contributes to self-initiated lifelong learning, participant responses with specific relevance to mental health are analysed under a transformative learning framework. Results indicate that digital media 1) often takes on a personal nature, resulting in reflections on experiences of trauma; 2) may present an inequitable distribution of negative impacts to individuals who already face marginalisation; 3) is accessed by participants in search of community, while simultaneously creating notable isolation; 4) is affected by the construction of ideologies around digital media, which potentially undermines its potential as a learning tool. Towards supporting the creation of digital media which successfully reduces inequalities in lifelong learning, 18 design considerations conclude this article.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".