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Record W2021864634 · doi:10.5539/ass.v11n12p62

Community Involvement in Urban Environmental Management System

2015· article· en· W2021864634 on OpenAlexvenueno aff
Hamidi Ismail, Tuan Pah Rokiah Syed Hussain, Mat Noh, Muhammad Subhan

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessGovernment (linguistics)Environmental planningVulnerability (computing)Urban managementUrban communityEnforcementEnvironmental resource managementLocal governmentUrban planningUrban ecosystemEnvironmental management systemPolitical scienceGeographySocioeconomicsPublic administrationEngineeringSociologyEnvironmental science

Abstract

fetched live from OpenAlex

The government of Malaysia has implemented various measures in the environmental management system, such as organizing an urban environmental management program. However, the environmental degradation problem in urban areas in the country is still alarming especially associated with the community engagement in protecting the urban environment. This study attempted to identify the level of community involvement in urban environmental management program in Malaysia based on 320 respondents. The results showed that the planning and enforcement activities by the community in the urban environmental management program were very low. In fact, the study also found that all the respondents in this survey did not execute their monitoring activities due to they assumed that the task should be undertaken by the government. The study concluded that contributing factors to the impairment of urban ecosystems in the study area is due to the vulnerability level of community involvement. Therefore, a better urban environmental management system is important to ensure higher community involvement in the urban environmental management program in strong collaboration with other concerned parties, especially the relevant government departments and the local authority.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.279
Teacher spread0.227 · 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.

Study designQualitative
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
Published2015
Admission routes1
Has abstractyes

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