MétaCan
Menu
Back to cohort
Record W7133014639

The Adoption, Use, and Impacts of Ride-sourcing Services in the Metro Vancouver Area

2022· dissertation· W7133014639 on OpenAlexaffabout
Felita Ong

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsHudbay Minerals (Canada)
Fundersnot available
KeywordsWork (physics)Metropolitan areaNoveltyPublic transportTransportation planningMode choiceJourney to work
DOInot available

Abstract

fetched live from OpenAlex

The advent of a new travel mode can transform travel behaviour and urban transportation systems. While ride-sourcing services provided by Transportation Network Companies have generally been established in most urban areas, they only recently became available in Metro Vancouver, Canada. Given the relative novelty of ride-sourcing in the region, it is important to understand the impacts of its introduction. This thesis aims to examine the factors influencing the adoption and use of ride-sourcing services and the impacts of said services on the use of other modes in Metro Vancouver through a web-based survey administered to residents of the region. Additionally, this thesis presents the results of empirical work performed using the collected survey data. The results can help planners understand the role that ride-sourcing plays and its relationship with other travel modes. The results can also be used to develop policies that harness the benefits of ride-sourcing.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueTSpaceSame topicTransportation and Mobility InnovationsFrench-language works237,207