AIRLINE CORPORATE GOVERNANCE AND SUSPICION OF CORRUPTION IN SUPPLY CHAIN MANAGEMENT: THE CASE OF TUNIS AIR AIRLINES
Bibliographic record
Abstract
The corporate governance of airlines companies is not a well studied area. As corruption is a criminal act, the issue is sensitive, and the doors are not opened for researchers. This article aims to contribute to the scarce literature surrounding this topic in order to overcome the lack of research. By staying studying this topic at a general lever and analyzing most of the supply chain activities of real airline companies, we can detect which activities within the supply chain present the most potential where corruption could take place. Propositions will be developed about the characteristics that need to be built into the value chain activities to be robust against corruption. These analyses and propositions are grounded in a case study of the Tunisian airline, Tunis Air. Although this company is not among the biggest airlines in the world, it’s governance could be a structural example in this area of study.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".