Anti-Corruption Institutions without an Anti-Corruption Curve: Assessing Ghana’s Governance Architecture, 2019–2025
Bibliographic record
Abstract
Ghana has expanded its anti-corruption efforts from 2019 to 2025 in ways that, in theory, should lead to an apparent decrease in corruption levels and an increase in global governance scores. The establishment and operation of the Office of the Special Prosecutor (OSP), the activation of the Right to Information (RTI) Commission, and repeated audits of payroll and public financial management (PFM). The ongoing presence of the Auditor-General, EOCO, CHRAJ, and Parliament’s Public Committee all suggest a growing network of accountability. However, Transparency International’s Corruption Perception Index (CPI) shows Ghana remaining between 42 and 43 points out of 100 for most of this period, dropping to 42 in 2024. Citizen data from Afrobarometer also indicates declining trust in key public institutions. This paper explores this paradox. Through qualitative analysis of official reports, survey data, and secondary literature on African anti-corruption agencies, it argues that Ghana’s issue is not a lack of institutions but a weak capacity to turn investigations, audits, and administrative findings into visible, enforceable outcomes. The paper recommends shifting from creating many institutions to strengthening and consolidating existing ones. It suggests that performance-based funding, RTI–budget integration, and judicial expedited processing of OSP cases are the next steps for Ghana’s governance reform.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".