Exploring equity implications of online grocery, online restaurant delivery and e-shopping service usage in a suburban context
Bibliographic record
Abstract
By examining how different demographics engage with online services, researchers and policymakers can better understand patterns and disparities in their access, usability, and engagement. This study explores the factors driving the frequent usage of online services, particularly online grocery shopping, online restaurant delivery, and e-shopping. By utilizing a representative sample of Scarborough, Ontario, Canada, collected in 2022, this study followed a Generalized Joint Regression Modelling approach by simultaneously modelling three online service usage behaviours. The findings suggested that millennials are highly likely to be frequent users of all forms of online shopping, while baby boomers and the greatest generation are less likely to engage in these activities. Households with children demonstrate a strong inclination towards online service usage, highlighting household's need for convenience and time savings. Access to personal vehicles influences online service usage behaviour. The study also found that car users are more likely to prefer in-person grocery shopping. Health-related challenges, such as mobility difficulties, correlate with increased reliance on online services. Furthermore, neighbourhood satisfaction and the perceived ease of accessing services positively impact online service usage. The findings further implied a nuanced relationship between online service usage and its potential impact on equity-deserving groups and sustainable transportation behaviour of Scarborough residents. Although findings suggested that online service usage is prevalent among several sociodemographic groups, it may exacerbate disparities for lower-income and transport-disadvantaged populations due to costs and digital exclusion. This scenario highlights the need for balanced urban planning and policy interventions to support equity and community ties in the digital age.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".