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 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 | 0.137 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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; both teacher heads agree on what is shown here.
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".