MétaCan
Menu
Back to cohort
Record W3200747040 · doi:10.1016/j.urbmob.2021.100006

Employer-paid transit subsidies and travel behaviour: Experimental evidence from Vancouver hotels

2021· article· en· W3200747040 on OpenAlexaffabout
Peter Hall, Anthony Perl, Karen Sawatzky, Steve Tornes

Bibliographic record

VenueJournal of Urban Mobility · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSubsidyBusinessTransit (satellite)TourismTransport engineeringUrban transitEconomicsPublic transportGeographyEngineeringMarket economy

Abstract

fetched live from OpenAlex

We report findings from an experimental study of the impacts of employer-paid transit subsidies on workers at downtown hotels in Vancouver, British Columbia, Canada. Partnering with the union and management of seven hotels, the regional transit agency and city government, we collected representative surveys of commuting behavior in three waves, each six months apart, in 2018 and 2019. Four of the hotels had offered a 15% transit subsidy prior to the study. We grouped six of the hotels in three similarly located pairs with the same initial subsidy condition. After the first survey wave, we provided an experimental subsidy at four hotels: 25% at one hotel in each of three pairs, and 15% at the seventh hotel. After the second survey we further increased the subsidy to 50% at two hotels. The larger the transit subsidy offered, the more employees become transit riders and the more transit-only commuting increased. Overall, a modest increase in transit-only commuting was accompanied by a reduction in auto-only and auto-and-transit commuting. It appears that transit subsidy acceptance and effectiveness can be dampened by factors such as the availability of cheap parking, or greater distance between the workplace and rapid transit, leading to variability in outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.312
Teacher spread0.275 · 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 teacher head, 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

Citations4
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Urban MobilitySame topicUrban Transport and AccessibilityFrench-language works237,207