The Guidelines of Corporate Governance of Ghana: Issues, Deficiencies and Suggestions
Bibliographic record
Abstract
The worldwide mushrooming of codes of corporate governance has resulted in a formalisation of rules andnorms. These rules and norms have undeniably been proven to be important constituents in shaping the presentcorporate governance structures across the globe. The 2010 Code of best practices for corporate governanceissued by the Securities and Exchange Commission of Ghana is an archetypical of such codes. It is irrefutablythe most encyclopaedic guideline for good corporate governance practices presently in Ghana. It containsoverarching provisions for effective corporate governance practices in Ghana. This paper presents a painstakingreview of the code. It first examines key properties of the code. Next, the main deficiencies of the code areilluminated by examining the provisions that lack clarity. Also, other unqualified derelictions, substandardprovisions and common gaffes in the code are highlighted. It then makes suggestions with the idea that theywould be taken into consideration if future remediation of the code is to be undertaken.
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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".