MétaCan
Menu
Back to cohort
Record W2507414157 · doi:10.1139/cjce-2016-0091

Conditions contributing to the attitudes for toll facilities in the United States with a specific focus on Virginia

2016· article· en· W2507414157 on OpenAlexvenueno aff
Changju Lee, John S. Miller

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
FundersU.S. Department of Transportation
KeywordsTollRevenueToll roadPopularityBusinessConsistency (knowledge bases)EconomicsMarketingPublic economicsFinancePolitical science

Abstract

fetched live from OpenAlex

Although the form of toll facilities has evolved, a review of how they have been used in the United States since its early colonial period suggests four conditions that appear to have influenced the likelihood of tolls being used to support construction or maintenance activities: the relative stability of revenue streams from user fees compared to the stability of revenues from a general tax; the availability of technologies to collect tolls without degrading the user’s experience; the presence of design innovations for toll facilities (compared to non-toll facilities); and the relative size of market benefits (for toll facilities) compared to societal benefits (for non-toll facilities). Even though revenue is one motivation for having a toll facility, other factors help explain why the popularity of toll facilities has risen or fallen. During the late 1800s, the network benefits of a smooth surface appealed to a large group (bicyclists) and generated a popular demand for public facilities. Yet in the early 1940s, another innovation—consistency of geometric design—spurred a market among paying customers for limited access highways. Collectively, such factors support five periods that characterize different public attitudes toward toll facilities: colonial/early federal period (from 1607 to 1775), turnpike era (from circa 1792 to 1845), toll reluctance era (from 1879 to 1939), post-World War II era (from 1939 to 1963), and renewed interest in tolling period (from circa 1976 to the present).

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.910
Threshold uncertainty score0.538

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicTransportation Planning and OptimizationFrench-language works237,207