Bibliographic record
Abstract
Purpose The paper aims to reveal some uncertain correlations and presumptions about corruption. Design/methodology/approach The paper defines corruption as a social phenomenon. It presents two basic components of that phenomenon: unreasonable preferential treatment and abuse of power. The paper addresses the moral issue that is implied in any phenomenon of corruption. The author will use the Corruption Perception Index and the Bribe Payers Index of Transparency International as well as the International Country Risk Guide, in order to check to what extent some correlations or presumptions about corruption could be reliable, at least as hypotheses. Findings Uncertain correlations and presumptions about corruption actually create an effect of distorted interpretation. They could cause ideological biases that distort our perception of corruption in developing and developed countries. Research limitations/implications The paper does not take into account the multiple expressions of gift‐giving practices around the world and the way such practices could be confused with corruption. Practical implications Being aware of our “presumptions” about corruption will help us to choose relevant strategies to combat corrupt practices. This study has implications for business corporations, governments and IFIs. It reveals how the awareness of such uncertainties and presumptions about corruption is related to the CSR discourse. Originality/value The originality of the paper is to unveil some presumptions about corruption that have not been compared with the results obtained from the Corruption Perception Index, the Bribe Payers Index and the International Country Risk Guide.
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 | 0.029 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".