East Coast Urbanites vs West Coast hippies – understanding differences in attitudes towards public transport
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".