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Record W2967790769 · doi:10.1177/1048291119867750

Travel Time as Work Time? Nature and Scope of Canadian Labor Law’s Protections for Mobile Workers

2019· article· en· W2967790769 on OpenAlexafffundabout
Dalia Gesualdi‐Fecteau, Delphine Nakache, Laurence Matte Guilmain

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2019
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of OttawaUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLegislationScope (computer science)Work (physics)Travel timeRepresentation (politics)Working timeLabour lawDemographic economicsLabour economicsPolitical scienceLawEconomicsTransport engineeringEngineeringPoliticsComputer science

Abstract

fetched live from OpenAlex

The spectrum of employment-related geographical mobility ranges from hours-long daily commutes to journeys that take workers away from home for an extended period of time. Although distance and travel conditions vary, there is a strong consensus within existing literature that mobility has physical, psychological, and social repercussions. However, is time spent traveling considered as working time? This question is crucial as it dictates whether or not workers can effectively access different sets of labor rights. The objective of this paper is twofold. First, contributing to a deeper understanding of travel time by offering a more sustained and complex representation of the various employment-related travel schemes. Second, assessing the circumstances under which travel time counts as work time with regard to the employment standards legislation in force in four Canadian provinces: Quebec, Ontario, Alberta, and British Colombia.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.702

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0110.013
Scholarly communication0.0070.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.025
GPT teacher head0.371
Teacher spread0.346 · 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 designNot applicable
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

Citations7
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueNEW SOLUTIONS A Journal of Environmental and Occupational Health PolicySame topicEmployment and Welfare StudiesFrench-language works237,207