Monetization of politics and public procurement in Ghana
Bibliographic record
Abstract
Purpose This paper aims to investigate the prevalence of corruption in Ghana to understand how and why it has turned public procurement into a mere money-making scheme instead of a means to provide needed public goods and services. Design/methodology/approach The study focuses on Ghana as a case study and mobilizes the monetization of politics lenses. Data are collected via interviews with key officials across the procurement sector (including the government, donors and civil society), documents, documentaries and news articles. Findings The findings suggest that the increasing costs of elections and political financing coupled with the costs of vote-buying, which has become informally institutionalized, intensify corruption practices and, consequently, turns public procurement into a mere source of cash for political ends. Political appointments and legalized loopholes facilitate this by helping to nullify the safeguard accounting and other control institutions are designed to provide. Likewise, enduring poverty and rising inequality “force” citizens into a vote-buying culture which distorts democratic premises that may drive out unscrupulous politicians; thus, perpetuating capture schemes. Civil society's efforts to remedy these have had little success, and corruption and inequality remain rife. Practical implications The main practical implication of the study lies in the need for a gradual demonetization of elections, and the consideration of the fundamental function of public procurement as a policy instrument embedded in economic, social, cultural and environmental plans. Additionally, given the connectedness of the various corruption issues raised, a comprehensive system-based approach in dealing with them would be more effective than a piecemeal approach targeting each issue/problem in isolation. Originality/value While extant literature has examined the issue of endemic corruption in developing countries using state capture, few have attempted to explain why it remains enduring, particularly in public procurement. This study, therefore, contributes to the literature on corruption and state capture theoretically and empirically by drawing on monetization of politics from political science to explain why corruption and state capture endure in certain contexts (with Ghana as an illustrative example) which reduce public procurement to a cash-milking scheme.
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 | 0.003 | 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 | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".