Does compassion go viral? Social media, caring, and the Fort McMurray wildfire
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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