An Evaluation of the Effectiveness of Governance Boards of Selected Not - for - Profit Organizations ( Npo's ) in Namibia
Bibliographic record
Abstract
Governance is defined as “the act of establishing and monitoring the long-term direction of an organisation through policy” (Alberta Culture and Community Spirit, 2008: 31). Consequently, good governance and code of good practices have become topical issues in organisations. Besides, good governance entails a thorough knowledge of all factors, both internal and external, which dictate the success of an organisation. It is consequently that the legal implications of establishing and maintaining an organisation are the responsibilities of the board. If a not-for-profit organisation (NPO) is not complying with legal provisions, the directors of the board are accountable. However, boards or trustees in the NPOs are largely volunteers. Unlike their counterparts in the private sectors and state-owned enterprises (SOE) in Namibia, NPO boards are not readily paid sitting allowances or preparation and retention fees. However, most NPOs reimburse travel, accommodation and lodging costs, while others do not pay at all. Despite this difference in remunerations, all boards/trustees are expected to discharge their functions and fiduciary responsibility; making decisions in the best interest of their respective organisations and ensure them to be good corporate citizens.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".