Framework for Screening and Evaluating the Competencies and Qualities of the Board of Directors in South Africa’s State-Owned Companies
Bibliographic record
Abstract
Purpose—This research paper presents a framework for screening and evaluating the competencies and qualities of the board of directors in South African state-owned companies (SOCs). Design/methodology/approach—This study conducted a systematic literature review to gather primary data which was used to prepare a questionnaire for two rounds of the Delphi process, where data was analysed both qualitatively and quantitatively. Findings—The findings from the study revealed the ideal competencies and qualities of individual directors, the optimal collective competencies of directors, and the most appropriate screening and evaluation methods that could be adopted to benefit SOCs. Originality/value—This paper adds to the limited studies investigating the competencies and qualities of directors in SOCs, as most research is focused on listed private companies. Furthermore, there is currently no framework in South Africa that outlines the process for screening and evaluating the competencies and qualities of directors in South Africa’s SOCs. In an effort to support the South African government screen and evaluate the key competencies and qualities of directors in state-owned companies, this team has developed a theoretically informed framework that can be used to screen potential board members’ abilities and capabilities before they are appointed as well as to evaluate the relevance of existing board members.
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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 | 0.076 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.025 | 0.008 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".