A Social Entrepreneurship Case Study of the “Pertubuhan Kebajikan Anak Yatim Islam Segamat” Orphanage in Malaysia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".