Photovoice, emergency management and climate change: a comparative case-study approach
Bibliographic record
Abstract
Photovoice aims to enable people to record and reflect their community’s strengths and challenges, to encourage group dialogue and knowledge about important issues through group discussions and to inform policymaking. While primarily utilized in the health field, an emerging area of focus is to use photovoice in an emergency management or climate change context. Through work conducted in two rural areas recovering from natural disasters in Ontario, Canada, this research, focused on critical infrastructure disaster recovery, underscores the value of undertaking a comparative case-study approach and offers a detailed reporting of the fieldwork methodology. We argue that photovoice has the potential to solicit poorly understood rural and Indigenous community member perspectives, thereby augmenting locally relevant, place-based information and, ideally, empowering voices that are often under-represented in municipal and provincial decision-making processes. We offer lessons learned related to the project’s processes and outcomes, and outline the applicability of photovoice for emergency management and climate change research.
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.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".