The Perception of Donors on Existing Regulations and Code of Governance in Singapore on Charities and Non-Profit Organizations – A Conceptual Study
Bibliographic record
Abstract
Non-profit organisations (NPOs) are meant to serve the public rather than to earn a profit for its members.Charities are also NPOs and have philanthropic goals as well as social well-being. It is imperative that theseorganisations to observe good corporate governance to safeguard the interest of the public and donors. Corporategovernance consists of a number of good elements such as trusts, clear vision, mission, direction, transparency,internal control, sustainability and corporate social responsibility. There were a number of charities andnon-profit organisations in Singapore which has flaunted the regulations and lost the trust of the public.Singapore has implemented various measures and regulations to govern these charities and NPOs. Itcontinuously upgrades the code of governance and educates the charity and NPO sector on the need for goodgovernance. However, irregularities seemed persistent. Donors, who are the main contributors to the existence ofthese charities and NPOs, had to have a say in the regulation of such charities and NPOs. Donors’ perception andexpectation have to be addressed so as to achieve an effective set of governance principles which do not overregulatethe charities and NPOs. Based on the findings of extant literatures and surveys, it was found that donors’perception on corporate governance has not been evaluated sufficiently. Singapore is improving the awareness ofgood governance among its charities and NPOs but has not looked at governance in donors’ point of view. Thispaper stresses the importance of donors’ perception in view of existing regulations and code of governance inSingapore charities and NPOs.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".