Remote, on-site, or flexible: hybrid choice modelling of urban–rural heterogeneity in work arrangements
Bibliographic record
Abstract
Amid the diversification of work arrangements post COVID-19 and rapid advances in information and communication technologies (ICTs), this study examines how urban and rural individuals in Nova Scotia choose between on-site, hybrid, and remote work. Employing hybrid choice models using the 2022–23 NovaTRAC survey data, the analysis explores the urban–rural divide in the constraining and facilitating factors influencing work arrangement preferences. The results confirm that sociodemographic, dwelling, daily activity, mobility, and accessibility attributes, as well as latent attitudinal factors, are significant determinants of work mode choice across both regions. Urban residents with digital lifestyle orientations and reduced auto dependency are more inclined toward remote and hybrid work, respectively, while in rural areas, flexible work advocacy predicts hybrid preference, and private lifestyle orientation is linked to fully remote work. These latent attitudes are further shaped by age, household size, and education. Moreover, rural residents with greater discretionary and maintenance activity needs are more likely to prefer hybrid work. Lower-income rural residents tend to work on-site, even when employed in urban areas. Urban residents without private vehicles and with limited transit access are more likely to remote work. Individuals highly engaged in non-work online activities show stronger preferences for telework in both regions. Shorter working hours and long-distance auto commutes further reinforce the likelihood of remote work. Complementary pseudo-elasticity simulations highlight that the magnitude of these effects differs markedly across urban and rural contexts, with commuting burden and reduced work duration exerting disproportionately strong impacts in rural settings.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".