Financial controls to control corruption in an African country: Insider experts within an enabling environment
Bibliographic record
Abstract
Abstract This study analyses an implementation of a government accounting reform in Benin directed at redressing fraudulent and corrupt practices. Although reforms to improve public administration and to mitigate corruption in Africa often have disappointing outcomes, our case study involving systems for payment of supplier invoices, payroll matters, and debt certificates had encouraging findings. The systems reduced inefficiencies and corrupt practices. An “enabling environment” (its main elements being emancipatory space, empowered participation, and ethical leadership) encouraged the deeper involvement of committed, expert, and ethical local civil servants in establishing effective financial controls. In the context of anticorruption reforms, this illustrates that public sector organizations in Africa should not invariably be regarded as monolithic bureaucratic top‐down entities, staffed by civil servants who are either passive “bystanders,” purely self‐interested “players,” or insufficiently expert, and hence in need for more training, and of imported, expensive, accounting systems implemented by foreign consultants. In contrast, the paper argues that, within a suitable environment, granting indigenous experts enough latitude to enact incremental yet substantive accounting changes at the local level may be more effective.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".