LEGAL FRAMEWORK FOR NON-PROFIT ORGANISATIONS IN MALAYSIA AND THE NEED FOR A STANDARD REGULATORY & COMPLIANCE FRAMEWORK IN CHARITY GOVERNANCE
Bibliographic record
Abstract
Non-Profit Organisations (NPO), also known as Non-Governmental Organisations or Charitable Organisations in Malaysia are governed by multiple laws and regulatory bodies, each with distinct compliance requirements. This study examines the available legal frameworks for NPOs in Malaysia and its compliance requirements, conducted through doctrinal and non-doctrinal analysis of the current legal framework and incorporating survey with key stakeholders to explore governance challenges. The existence of multiple laws creates confusion amongst the public regarding standard terms like "Foundations" as each type of NPO has unique registration and compliance criteria. While these multiple legal spheres offer flexibility in choosing suitable model for registration, they also highlight the need for standardisation and uniformity to enhance governance practices. With the current demand for the third sector and Social Finance, this study underscores the urgency of streamlining regulatory frameworks to foster better charity governance practices, drawing insights from well-presented Islamic Economic models throughout history.
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 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 | 0.039 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".