On the Selection of Workplace in the COVID-19 Era Using Attitudinal Factors
Bibliographic record
Abstract
After two years of living with the threat of COVID-19 in Ontario, Canada, pre-pandemic circumstances returned. During the pandemic, we relied on ICT-based tools to carry out our daily tasks, and now we have reached a tipping point. Should we keep our new routines to benefit us in the future? Or should we return to our routines before the pandemic? This study utilized a travel survey to examine the impact of the COVID-19 pandemic on work activity-travel behavior and the persistence of new traditions in the post-pandemic era. The data for this study comes from a sample of 1,000 Greater Toronto Area residents who participated in a web-based survey in July, 2021, when Ontario began the third phase of reopening. This paper investigates work activity and workplace selection in more depth. Using factor analysis, a collection of latent attitudinal variables was identified. An integrated choice and latent variable model estimated the influential systematic and latent variables on the perceived workplace selection in the post-pandemic condition. The results showed that 71% of responders want to continue working from home at least once weekly after the pandemic. The preferred frequency of telecommuting in the post-pandemic period was positively correlated with education level, positive feelings about telecommuting experience, and certain occupation types, while negatively associated with age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".