Impacts of the 2013 Flood on Immigrant Children, Youth, and Families in Alberta, Canada
Bibliographic record
Abstract
The 2013 flood resulted in devastating impacts for immigrant children, youth, and families in Alberta, Canada. This article presents the findings of the Alberta Resilient Communities (ARC) Project, a collaborative research initiative that aimed to better understand the social, economic, health, cultural, spiritual, and personal factors that contribute to resiliency among children and youth. The study findings indicate that immigrant children and youth resilience is tied to four main themes: 1) Constructive parental responses; 2) Effective school support; 3) Active involvement in/with community; and 4) Connections between disasters and the environment. Community influencer participants revealed flood recovery challenges experienced by immigrant families that affected their settlement and integration at the community level. Major themes include: (1) Loss of documentation; (2) Provision of temporary housing and accommodation; and (3) Rethinking diversity in disaster management. The study findings demonstrate that immigrants faced significant socio-economic impacts, trauma, job loss, and housing instability as a result of the flood and its aftermath. Challenges such as limited social ties within and beyond the immigrant community, limited official language fluency, and immigration status contributed to their vulnerability. Immigrant children and youth with positive support from their immigrant parents were found to be more resilient, integrated, and engaged in the community. Recommendations for disaster and emergency management agencies to address diversity factors such as immigration status, language, age, and culture that shape long-term disaster recovery experience are provided. Schools, immigrant parents, and community connections were found to play a key role in fostering immigrant child and youth resilience post-disaster.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".