Framing of Youth as a high-risk population in Canadian disaster news media
Bibliographic record
Abstract
Community disaster risk reduction practices have been tested repeatedly in the past decade. In Canada, for example, Ottawa-Gatineau and cities across Alberta have experienced numerous disasters, garnering widespread media coverage. Along with their families, youth were impacted by these disasters, but they also contributed to the response and recovery efforts in their communities. In this paper, we present a critical discourse analysis of the journalistic coverage of five Canadian disasters, including the Alberta floods (2013), Fort McMurray wildfires (2016), Ottawa-Gatineau flooding (2017 and 2019), and the Ottawa-Gatineau tornadoes (2018). This study is part of a broader project focused on the representation of high-risk populations in disaster-media. In this paper we discuss how disaster-media portrays youth, and their contributions to disaster preparedness, response and recovery. We inductively analyzed 259 articles across the five disasters to identify and document the patterns of discourse in news media around youth in a disaster context. Our findings reveal that the Canadian disaster news media framed youth using fives lenses: 1) the vulnerable status of youth; 2) youth as passive bystanders; 3) adult-centered narratives in media coverage of disasters: children as a burden on adults; 4) youth as active agents – jumping into adulthood; and 5) youth as a ‘legitimizing criteria’ in disaster response. The results of our study point to a need for a shift in the framing of youth in disasters to highlight their assets and actual/potential roles in disaster risk reduction efforts. Media can help to shift the narrative around youth by avoiding reductive, one-dimensional representation of youth as vulnerable victims.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".