<i>Keyboard warriors</i>? Visualising technology and well-being <i>with, for</i> and <i>by</i> indigenous youth through digital stories
Bibliographic record
Abstract
The Young Lives Research Laboratory is concerned with the lives of modern youth from education to technology to mental health. Technology is ubiquitous in the day to day lives of young people in Canada but little is known about the ways in which digital media affects their mental health, especially for Indigenous youth. Research seldom engages youth to clarify and or interrogate digital media and well-being. This paper addresses the dearth of empirical work and supports the development of practices which better reflect and address health impacts of digital technology on young lives. In using an empowering participatory process to provide Indigenous youth opportunity and tools to produce short digital story films helped them to reflect their unique experiences with digital media and its role in both personal and community well-being. The study also explores youth-produced filmmaking as an effective medium for communicating technology-related experiences and challenges. We share youth-produced films to exemplify the cultural process and products that arose in this project.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.021 |
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".