Understanding work-arrangement choices: factors and implications
Bibliographic record
Abstract
Information and communication technologies (ICTs) have spurred new work arrangements, yet factors influencing these choices remain unclear. This study employs mixed-logit modeling to investigate the determinants of work arrangements—’fully work-from-home (WFH),’ ‘hybrid,’ and ‘no WFH’ – and their impact on activity-travel behavior. Conducted in Halifax Regional Municipality, Nova Scotia, Canada, the study combines travel survey data with Census and built-environment data for analysis. Significant differences are found in activity count, work duration, vehicle kilometers traveled, and commute time among the work-arrangement groups. ‘Hybrid’ and ‘no WFH’ individuals tend to reside closer to downtown, while ‘full WFH’ individuals prefer suburban and rural areas. Results identify individual, household, and accessibility attributes as key determinants, confirming random heterogeneity among respondents. Results suggest shorter auto commute times correlate with higher likelihood of ‘no WFH’ and lower likelihood of ‘full WFH.’ This research aids policymakers and transportation professionals in developing effective travel demand management strategies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".