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Record W3039876228 · doi:10.5430/ijfr.v11n3p162

A Social Entrepreneurship Case Study of the “Pertubuhan Kebajikan Anak Yatim Islam Segamat” Orphanage in Malaysia

2020· article· en· W3039876228 on OpenAlexvenueno aff
Saunah Zainon, Rina Fadhilah Ismail, Soo Kum Yoke, Haryati Ahmad, Nurulzulaiha Suhadak

Bibliographic record

VenueInternational Journal of Financial Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipIslamSociologyState (computer science)Social entrepreneurshipEconomic growthDemographic economicsPolitical scienceEconomicsGeographyLaw

Abstract

fetched live from OpenAlex

Most orphanages in Malaysia are run by means of charitable donations. However, the donations and contributions collected are usually not regular or one-off. This has led to orphanages being in a run-down state and ill-equipped. The care given to orphans is important as part and parcel of their human rights to be treated and given a fair chance of living with the rest of the human race. As such, this paper aims to investigate the role of social entrepreneurship in providing care for orphans. Particularly, the paper will focus on an orphanage – “Pertubuhan Kebajikan Anak Yatim Islam Segamat” (PKAYIS) which is located in the state of Johor. PKAYIS has provided shelter for 68 orphans and through charitable donations has been running successfully since 1983. The orphanage has been able to successfully bring up orphans who have succeeded academically and found successful careers in life. Based on the theoretical framework on social entrepreneurship and orphans, observation methods and interviews were carried out to supply relevant information and data for the study. This paper will provide some insights on how social entrepreneurship affects the society and would be beneficial for policy makers interested in adopting social entrepreneurship as a mean of care for orphans.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.162
GPT teacher head0.467
Teacher spread0.305 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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