A Case Study of the COVID-19 Pandemic Crisis Response: Serving Racialized Immigrant Communities
Bibliographic record
Abstract
Drawing on Calgary East-Zone Immigrants Collaborative (CENC) as a case study, this research examines the role of settlement organizations as part of the collaboration in responding to the COVID19 pandemic in Calgary. Adopted a community-based and cross-sectoral model of crisis response that addresses community connections (Eisenman et al., 2007; Leong et al., 2015) and temporary partnerships between multiple stakeholders (Bryson et al., 2006), findings in this study highlight the following perspectives: First, settlement organizations have a culturally diverse, multilingual staff, and extensive experience communicating with non-English speakers, crucial resources that were mobilized and translated for COVID-19 crisis response. These contexts facilitated CENC to demonstrate services with linguistic and cultural capacities, offer culturally appropriate food delivery, and provide culturally competent financial assistance and guidance. Second, settlement organizations between state agencies and grassroots communities were formed through one-on-one and small group conversation initiatives and pivoted for facilitating a crisis response to form long-established trust relations with immigrant communities. Thirdly, the CENC response involved in large part a repurposing of settlement organizations’ existing logistical and technological capabilities or infrastructure from financial literacy and job training services to public assistance benefits and unemployment support services, database infrastructure to referral systems, and catering to food delivery to meet shorter-term needs brought on by the pandemic. This study concludes that public agencies’ rapid crisis response support merged with settlement organizations’ settlement support for more integrated services that were responsive to urgent needs unique to the COVID-19 pandemic and particular to specific needs unique to immigrant communities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".