Bibliographic record
Abstract
As the opioid crisis escalates across North America, photographers are highlighting the gravity of the situation. However, many of their images of people who use drugs are problematic and stigmatizing. This study looks at how digital storytelling (DST) was used in order to assist long-term heroin users taking part in North America's first heroin-assisted treatment program in Vancouver, BC, in amplifying and sharing their personal experiences. DST is a participatory and collaborative process designed to help people share narrative accounts of life events. A total of 10 participants took part in a 3-day DST workshop and eight individuals completed 2 to 3-minute digital stories. Participants demonstrated increased agency in terms of how they represented themselves. Their digital stories disrupt hegemonic representations of heroin users and can help educate the public and decision makers about compassionate and science-based treatments for chronic addiction. Theory, methodology, practical applications, and ethics are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".