The Competent Leaders of the Saudi Non-Profit Organizations
Bibliographic record
Abstract
To attain Saudi Arabia’s Vision 2030’s goal of developing leadership, we need to understand the key issues that have emerged in the context of learning skills relating to leadership in non-profit organizations (NPOs) to enhance their efficacy for management positions. This study elaborates upon the relevant abilities of leaders of NPOs. A qualitative approach involving interviews with 15 directors from renowned non-profit Saudi organizations was used. These 15 leaders, together with 6 other employees from NPOs participating in a focus group, were the primary informants. The study involved semi-structured interviews with professionals from non-profit sectors such as education, health, environmental sustainability, and human resources to summarize critical elements that either help facilitate or negatively affect these individuals’ ability to contribute to institutional results. The results revealed six competency groups – namely personal, management, social, industry-specific, work-specific, and academic – which demonstrated the usefulness of the approach in gaining useful insights that would otherwise not have emerged. The results indicate that leaders of NPOs should exhibit these six levels of competencies to counter internal and external difficulties, and to effectively contribute to achieving organizational goals.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".