Does compassion go viral? Social media, caring, and the Fort McMurray wildfire
Bibliographic record
Abstract
In May 2016, an enormous wildfire threatened the city of Fort McMurray, Alberta and forced the evacuation of all of the city’s residents. Outpourings of support teemed in from all across Canada and over the world, prompting the largest charitable response in Canadian Red Cross history. This paper examines Albertans’ response to the wildfire by exploring caring and helping behaviors as well as the role of social media in facilitating these remarkable charitable efforts. The paper uses mixed methods including an analysis of the most popular Tweets related to the wildfire and an Alberta survey collected months after the disaster. The analysis of tweets reveals that care, concern, and invitations to help were prominent in social media discourse about the wildfire. The analysis of survey data demonstrates that those who followed news about the wildfire on social media express higher overall levels of care and concern for those affected, which led to helping those impacted by the wildfire. The findings provide important insights about the role of social media in disaster relief and recovery as well as citizens’ civic engagement.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".