The Lac-Mégantic Photovoice Initiative
Bibliographic record
Abstract
Background In 2013, a train carrying oil rolled down a mountainside, derailed and exploded in the town of Lac-Mégantic, Quebec; 47 people died, the downtown core was decimated, and many people lost their homes or business. In the 4 years following, the town has taken many steps toward recovery. As the downtown is being re-built, one initiative is a campaign to dissociate from the image of the inferno and reinvent the community. In this presentation, we present a Photovoice project which has the goal of giving power to the voices of the people in the Lac-Mégantic community to express their vision of a positive public relations campaign and their experiences with how this strategy supports long-term community recovery. Methods Photovoice is a qualitative method used in action research to engage participants in co-creating rich data about a topic that is important for their community (Wang & Burris, 1994). This Photovoice initiative is oriented around the following steps: 1) outlining the objectives, 2) recruiting participants, 3) an orientation session, 4) monthly discussion sessions, and 5) hosting a photo exhibition to present the photos and emergent themes to the community, policy audiences, decision-makers, and other stakeholders. Following approval from the university ethics review board, citizens from Lac-Mégantic were invited to participate in a Photovoice project; Seventeen people were recruited and are currently engaged in the project. Three Photovoice groups were created to accommodate scheduling and keep the group sizes between 4-8 people. Each month, the participants choose the photo assignment for their group, take photos, and bring them back for discussion. The sessions are audio-recorded and transcribed verbatim. Theme analysis is being conducted and brought back for discussion. Results In this presentation we will share our experiences of this post-disaster recovery initiative, present the themes and a summary of the photo exhibition. Key messages: Photovoice can be used to support resilience. Four years after the train disaster, Lac-Mégantic citizens are sharing their visions of what makes this community a great place to live.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.003 |
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".