MétaCan
Menu
Back to cohort
Record W2613793899

East Coast Urbanites vs West Coast hippies – understanding differences in attitudes towards public transport

2017· article· en· W2613793899 on OpenAlexaboutno aff
Greg Spitz, Stephane Hess

Bibliographic record

VenueInternational Choice Modelling Conference 2017 · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsContext (archaeology)Service (business)GeographyPublic transportScale (ratio)PerceptionProduct (mathematics)MarketingBusinessSociologyTransport engineeringEngineeringCartographyPsychologyDemography
DOInot available

Abstract

fetched live from OpenAlex

Hybrid model structures are becoming more common to integrate choice models with other model components to understand the role of attitudes and perceptions in choices beyond the standard variables of time, cost, frequency, etc. We believe these hybrid models are important, as it has been demonstrated that taste heterogeneity is influenced by factors beyond simply operational variables for a product or service.  However, applications for real world work (as opposed to academic research) are still relatively limited and many applications fail to provide convincing insights for policy makers. This paper provides a large scale application of ICLV models in a context where two groups of respondents from different areas and with different contexts are faced with similar choices. From a policy perspective, there is growing interest in encouraging a shift away from private cars to public transport in the United States, where the uptake remains low compared to almost all other developed nations. We specifically look at both East Coast and West Coast respondents, meaning that differences arise in terms of geography, experiences, culture, and socio-demographics. There are known and clear a priori segments in our study. Namely, the differences between Northeast Corridor (NEC, Boston to Washington DC through NYC) respondents, who live in an established urban “megaregion” and have access to intercity rail service akin to European rail service, as well as significant bus and air service. The options for intercity travel in the Northeast Corridor Amtrak are more robust than anywhere else in the US, and the rail service carries more passengers per day than competing air service, with about 40 trains per day in each direction and a top speed of 150 MPH. This is rare in the US. The culture in the highly populated NEC is urban and sophisticated and located in the oldest “settled” part of the US with three of the US’s largest and oldest cities (Boston, New York, and Washington, DC). This is where the “establishment” of the US is located (as the politicians say). Meanwhile, the Cascade Corridor respondents (Vancouver BC to Portland, OR, through Seattle) live in a more typical American intercity corridor from a transportation perspective, with smaller cities and just a few intercity mode options. The cascade corridor’s intercity mode share is also more typical for America, with low rail market share for an infrequent train service of 5 trains per day in each direction and a top speed of 79 MPH, with much higher shares for air and auto modes, as well as some bus service. The culture in the pacific northwest is also quite different from the northeast. It still has a frontier/alternative feel and overall has a more outdoorsy/alternative culture, with arguably some of the best (if not the best) beer and coffee in the world, which is important due to low numbers of sunny day in the region. A comparison of results across generic and area specific models will allow us to answer not just questions in terms of differences across areas, but also whether these differences are intuitive, relate to socio-demographic, infrastructure, or attitudes.

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)0.003
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 score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.246
GPT teacher head0.350
Teacher spread0.104 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Choice Modelling Conference 2017Same topicTransportation Planning and OptimizationFrench-language works237,207