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Record W4200296573 · doi:10.1016/j.trip.2021.100513

Telecommuting and food E-commerce: Socially sustainable practices during the COVID-19 pandemic in Canada

2021· article· en· W4200296573 on OpenAlexaffabout
Janet Music, Sylvain Charlebois, Virginia Toole, Charlotte Large

Bibliographic record

VenueTransportation Research Interdisciplinary Perspectives · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTelecommutingDisadvantagedBusinessSustainabilitySocial distanceExploratory researchPandemicCoronavirus disease 2019 (COVID-19)Status quoMarketingEconomic growthPolitical scienceSociologyEconomicsMedicineEngineering

Abstract

fetched live from OpenAlex

Telecommuting has become a dominant professional experience for many Canadian business and workers due to the COVID-19 pandemic. Telecommuting has several benefits that are separate from COVID-19. Two prevalent changes have been in regard to telecommuting and online food buying habits, both of which impact social wellbeing as a dimension of social sustainability. We discuss two exploratory surveys on the perception of telecommuting and food e-commerce. We found that while telecommuting has the potential to increase social wellbeing and the social sustainability of both urban and rural Canadian communities through a variety of mechanisms, food e-commerce does not offer similar returns. Instead, the prevalence of food e-commerce merely adds convenience to the lives of those who already have adequate food access while maintaining the status quo, or even worsening access for disadvantaged Canadians.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.683

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0180.006
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.094
GPT teacher head0.393
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2021
Admission routes2
Has abstractyes

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