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Record W2169822216 · doi:10.5539/ass.v9n8p17

The Roles of Government Agency in Assisting CSR Project for Community Development: Analysis from the Recipients Perspectives

2013· article· en· W2169822216 on OpenAlexvenueno aff
Sarmila Md Sum, R. Zaimah, Novel Lyndon, A. M. Azima, Suhana Saad, Sivapalan Selvadurai

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityAgency (philosophy)Government (linguistics)Thematic analysisCommunity developmentPublic relationsCorporationBusinessQualitative researchPolitical scienceEconomic growthSociologyFinanceEconomics

Abstract

fetched live from OpenAlex

Corporate Social responsibility (CSR) initiatives by the corporations are playing increasingly significant role in the effort of contributing to community development particularly in the developing country. The international prominence of the initiatives in this area can be traced to the objectives of Millenium Development Goals (MDGs) that established corporations as partners of development. However, realizing the corporate sectors constraints in playing the roles as agents of development, the CSR initiatives need to be implemented with the involvement of the government agency to assist community development. This paper will explore the roles played by government agency in CSR project through a case study of successful CSR initiative in Bukit Awang, PasirPuteh Kelantan in Malaysia. A qualitative research strategy that explores the perspective of social actors that involved in the project and thematic data analysis are undertaken for that purpose. The result from the analysis reveals two main roles played by the government agency in CSR project by the corporation to the community. The roles are as the supporting agency and as leader in the community. These roles have assisted in the implementation of the CSR project with the objectives to develop the community involved.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
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 head0.023
GPT teacher head0.262
Teacher spread0.239 · 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 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

Citations10
Published2013
Admission routes1
Has abstractyes

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