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Record W4403827434 · doi:10.22610/imbr.v16i3s(i)a.4148

Navigating Urban Mobility: The Relationship between Car Consumption and Public Transport Usage in Malaysia

2024· article· en· W4403827434 on OpenAlexaff
Zuraidah Ismail, Saliza Sulaiman, Tuan Badrol Hisham Tuan Besar, Zaidi Mohd Aminuddin, Shahariah Asmuni, Noor Adwa Sulaiman, Syarul Emy Abu Samah

Bibliographic record

VenueInformation Management and Business Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPublic transportConsumption (sociology)BusinessEnvironmental economicsCar ownershipAdvertisingMarketingTransport engineeringEconomic geographyEconomicsSociologyEngineering

Abstract

fetched live from OpenAlex

Increasing private-owned cars in Malaysia has, thus far, contributed to growing congestion, environmental pollution, and decreasing public transport utilization. Car ownership has become possible through economic growth, and convenience and status attached to cars triggered tendencies from shifting to public transit. This development contradicts investments in public transport and aggravates environmental problems. This paper examines the relationship between private car ownership and public transport usage in Malaysia from 2000 to 2022, concentrating on intercity bus services, KTM commuters, and light rail transit. The results indicate that with increased riders for intercity buses and KTM commuter services, car usage drops, while that of light rail may raise car ownership. The study emphasizes the need to upgrade intercity and KTM services to mitigate the pressures of car ownership and calls for further research into the role of light rail.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.332
Teacher spread0.268 · 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
Published2024
Admission routes1
Has abstractyes

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