An Empirical Analysis of Emotions of Asian Indians During the First 100 Days of COVID-19
Bibliographic record
Abstract
India’s COVID-19 tally was almost 8 million on September 26, 2020, while the global cases marched towards 42 million. People globally have been living under some sort of COVID-19-driven lockdown, stay at home, shelter-in-place, or some similar government-mandated measures. Living in a lockdown is NOT a natural phenomenon for most humans. How are people trying to manage themselves during the restrictions imposed on them? What are the mechanisms people are resorting to, to cope with this stress? Is their heritage, cultural or national differences that are allowing one segment to react better than the other? To address the research questions presented here, we leverage the qualitative analysis technique of narrative research to understand emotions and actions of 25 people of Indian heritage (spread across India, USA & Canada) over the first 100 days of the COVID-19 pandemic and transpose the same with the Transactional Model of Stress and Coping. In this research, we present the initial findings from the ongoing study of global Indians and demonstrate that people are using a system of appraisal, response, and adaptation strategies. Both problem-focused and emotion-focused coping strategies are being leveraged, sometimes together. Reappraisal is driving a dynamic response to the evaluation and response process. Over 100 days, there is a shift from the physiological needs to the safety needs to love and belonging, and an overall shift from concern/panic to acceptance/co-existence. Practical, theoretical, and policy-related implications are discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".