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Record W2170630032 · doi:10.5539/jms.v4n1p154

Barriers to Practice of Non-Hazardous Solid Waste Minimization by Industries in Malaysia

2014· article· en· W2170630032 on OpenAlexvenueno aff
Shadi Kafi Mallak, Mohd Bakri Ishak, Ahmad Fariz Mohamad, Sabrina Ho Abdullah

Bibliographic record

VenueJournal of Management and Sustainability · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsHazardous wasteLikert scaleMunicipal solid wasteScale (ratio)Index (typography)MinificationBusinessAnalytic hierarchy processOperations managementWaste managementEngineeringOperations researchComputer sciencePsychology

Abstract

fetched live from OpenAlex

The Practice of waste minimization plays a significant role in sustainable development as the most acceptable method in the waste management hierarchy. This paper is a case study research on industrial non-hazardous wastes generated from different industrial activities in one of the major Malaysian industrial areas. This study is aimed at identifying the barriers of waste minimization practices in Malaysian industries. The combination of quantitative and qualitative methods were applied in the study through the use of a structured questionnaire prepared on Likert scale and semi-structured interviews with respondents across thirty (30) factories. Data collected through the questionnaire was analyzed using software and severity index tool. Findings reveal the barriers faced in practicing waste minimization by industries include the lack of time for separation of waste, absence of guidelines, regulations and limited accurate knowledge with severity index range of 62.5<=SI< 87.5, which were considered to be serious issues. Through the application of suitable educational and awareness programs for industrial stakeholders, an effective waste minimization practice can be achieved.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.613
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.004
GPT teacher head0.244
Teacher spread0.240 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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