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Impacts of river of life project towards the conservation of urban heritage quarter in Kuala Lumpur: a preliminary study

2021· article· en· W3197011339 on OpenAlexaboutno aff
Ainur Syazwina Zuraimi, Indera Syahrul Mat Radzuan

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2021
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Kuala lumpurSocioeconomic statusEnvironmental planningGeographyUrban planningEconomic growthSocioeconomicsEnvironmental resource managementCivil engineeringBusinessSociologyEngineeringMarketingArchaeologyPopulationEconomics

Abstract

fetched live from OpenAlex

Abstract The River of Life (RoL) is one project of Malaysia’s Economic Transformation Programmes. RoL is a multi-year attempt that integrates high-impact programmes to help Malaysia achieve a status of a developed nation. This study discussed the components of the waterfront development packages in the River of Life (RoL) project implementation. As explained by urban planning theory, the growth of a city is affected by multiple factors. The word “urban regeneration” originated to define the social, economic, and environmental components that influence a city’s growth. This study intended to examine the socioeconomic impact of the RoL project in the Kuala Lumpur heritage quarter in terms of development. A qualitative method approach combining interviews and document reviews were used in this study. The study revealed the impacts of RoL project within the heritage quarter from the perspectives of the local authorities, private sector as well as stakeholders involved in the project. The study findings will help numerous stakeholders to provide better urban living for the Kuala Lumpur heritage quarter.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.207
Teacher spread0.189 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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