The future of telecommuting post COVID-19 pandemic
Bibliographic record
Abstract
COVID-19 caused unprecedented changes in the daily lives of many people worldwide, with many working from home for the first time. This shift in working arrangement has the potential to have a lasting impact in future. This paper investigates longer-term impacts of COVID-19 on work-arrangements, specifically, individuals' preferences towards work-from-home post COVID-19. This study utilizes data from a stated preference component of a travel survey conducted in the Central Okanagan region of British Columbia. A random parameter ordered logit model is developed to accommodate the ordinal nature of the preference variable and capture unobserved heterogeneity. One of the key features of the study is to confirm the effects of residential choice in-terms of location characteristics and dwelling attributes on work-from-home preferences after the pandemic. For example, individuals' dwelling attributes such as larger sized dwelling, larger sized apartments are likely to have positive effect on frequent work-from-home. The model confirms significant heterogeneity, in relation to location characteristics such as commute distance and distance to urban center. For instance, initially, females were less likely to work-from-home. However, they showed significant heterogeneity with large standard deviation, specifically their preference was found to vary by residential location. For instance, females residing farther from urban centers prefer a higher frequency of work-from-home. Elasticity analysis suggests that part-time female workers, mid-age individuals, full-time workers with children, and full-time workers with longer commutes have a significantly higher probability to work-from-home every day after the pandemic. The findings of the study provide important insights which will assist in developing effective work-from-home strategies post-the-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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".