Exploring work-related cosmology events: lessons from the COVID-19 pandemic
Bibliographic record
Abstract
Purpose The present study examines personal accounts of disruptions and adaptations in the work lives of individuals who were required to work from home during the coronavirus disease 2019 (COVID-19) pandemic. Our objective was to better understand how individuals respond to rapid, unanticipated changes in their work conditions. Design/methodology/approach Our qualitative study reflects in-depth interviews with 56 individuals to investigate how they were thinking, feeling and behaving in the early stages of the COVID-19 pandemic. Findings Our results suggest that individuals engaged in sensemaking to adapt to remote work and reacted in two ways: backward-focused and filled with nostalgia for a past that might never re-emerge, or forward-focused and filled with anticipation to build a new way of work. Practical implications Understanding employees’ responses to a major disruption provides guidance for how to support and lead them back to stability. Building flexibility into organizational structures, as well as establishing more robust and personalized support mechanisms, may enable more adaptive and resilient coping in the future. Originality/value We contend that the rapid shift to working from home represents a pervasive cosmology event – an abrupt crisis that challenges our assumptions about what is normal. Studying the responses to such an event provides insight into how people make sense of disruptions to their working lives, determine how to react and act to re-establish order.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".