Participatory photography gives voice to young non-drivers in New Zealand
Bibliographic record
Abstract
Youth have the highest crash injury risk in New Zealand. Māori and Pacific youth have an even higher risk. Highlighting and promoting benefits of modal shift from cars to active and public transport may increase health and safety. We aimed to create a discussion surrounding transport issues to gain a better understanding of attitudes and behaviours of non-driving youth, to empower our participants and to promote health and social change by making participants' opinions and experiences known to the broader community through a public exhibition. We engaged nine non-drivers aged 16-24 years in photovoice. Through sharing their photos and stories, participants used the power of the visual image to communicate their experiences. This method is an internationally recognized tool that reduces inequalities by giving those who have minimal decision-making power an opportunity to share their voice. By the end of the project, it was clear that the participants were comfortable with their non-driving status, noting that public and active transport was more cost-effective, easy and convenient. This attitude reflects recent studies showing a marked decrease in licensure among young people in developed countries. This project uniquely prioritized young Māori, Pacific and Asian non-drivers.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".