The COVID-19 Pandemic: An Immigrant Family Story on Reconnection, Resistance, and Resiliency
Bibliographic record
Abstract
The COVID-19 pandemic has created a significant effect on the vulnerable portion of society, particularly on Indigenous and visible minority immigrants. We, as a minority family from Bangladesh who are on Indigenous land in Saskatchewan Canada, explore family-based pandemic resiliency, mainly focusing on Indigenous notions of resistance and reconnection. This article discusses our family-based resiliency on family interaction, social distancing, and isolation during the COVID-19 pandemic. This paper explores a family-based decolonizing autoethnography as a methodology for understanding health and wellness from an immigrant family’s perspective. We discussed why Indigenous and immigrant stories matters for building resiliency and resistance within a family. How do we know it is effective? How can it be helpful for others? Here, we highlight how Indigenous Elders, Knowledge-Keepers, and ancestors’ stories helped us for building our resistance and reconnection to be active, hopeful, and joyful during the COVID-19 pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".