Gift or bribe? The characteristics and the role of gift policies in the prevention of corruption
Bibliographic record
Abstract
Purpose This paper aims to explore the current trends in corruption and investigate the characteristics of corporate gift policies and their role in preventing bribery. Design/methodology/approach This is a descriptive study based on primary data from a recent sample of Canadian companies’ codes of conduct and secondary data from recent corruption surveys published by non-governmental organisations. Findings This study shows that 25% of all private and public corruption cases generate financial damages of more than US$1m per case and that 50% of all investigated fraud cases are corruption cases (ACFE, 2022). Furthermore, the Western Europe and EU region is perceived as least corrupt, whereas Sub-Saharan Africa is perceived as the most corrupt region (Transparency International, 2022). However, bribery is fairly common in nine EU countries where 10% or more of public service users bribed public officials to influence their decisions (Transparency International, 2021). Results from primary data show that 9.3% of firms put a total ban on gifts given to governmental officials, whereas 35.2% require a superior’s approval and only 5.5% state a dollar limit for the gift. Results also show that not a single firm prohibits the giving of gifts to non-governmental stakeholders or the receiving of gifts from any type of stakeholder. This paper argues that gifts can bias the recipient’s judgement and improperly influence future business decisions based on the gift’s subjective value, nature and context. Research limitations/implications This paper extends previous research by examining the characteristics of corporate gift policies. It also helps organisations improve their gift policies in an effort to reduce corruption. Originality/value It is the first paper to investigate the characteristics of corporate gift policies and their role in preventing corruption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".