Participatory photography gives voice to young non-drivers in New Zealand
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it