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Record W573199965 · doi:10.21949/1503571

Community-Oriented BRT: Urban Design, Amenities, and Placemaking

2015· article· en· W573199965 on OpenAlexaboutno aff
Jennifer Flynn, Menna Yassin

Bibliographic record

VenueRosa P: A digital library for transportation research (United States Department of Transportation) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
FundersU.S. Department of Transportation
KeywordsPlacemakingRealmBus rapid transitPublic spacePublic transportResource (disambiguation)Transit systemCommunity designPublic relationsSociologyUrban designUrban planningGeographyTransport engineeringPolitical scienceComputer scienceEngineeringArchitectural engineeringTransit (satellite)Civil engineeringArchaeology

Abstract

fetched live from OpenAlex

The purpose of this report is to provide a useful resource for communities that wish to learn how others have successfully used BRT as a tool for enhancing the public realm. Information for this effort was gathered through a literature review, in-depth profiles of three BRT systems, and a detailed questionnaire that was administered to transit agencies in the United States, Canada, and Australia. While the literature review provides historical background on the relationship between transit projects and the public realm, the questionnaire focuses specifically on the interaction between BRT and public space. The system profiles provide a detailed account of the Los Angeles Orange Line, Cleveland’s HealthLine, and the EmX in Eugene, Oregon, along with recommendations and lessons learned. It should be noted that this report does not attempt to offer detailed instructions of the type that would be found in design manuals or other highly technical literature. Rather, the focus is on sharing the experiences of agencies that have been successful in designing and building community value into BRT projects.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0040.004
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.001

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 head0.097
GPT teacher head0.344
Teacher spread0.247 · 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 designNot applicable
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

Citations1
Published2015
Admission routes1
Has abstractyes

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