Anti-Competition and Anti-Corruption Controversies in the European Financial Sector: Examining the Anti-ESG Factors with Entropy Weight and TOPSIS Methods
Bibliographic record
Abstract
(1) Background: This research aims to investigate the impact of environmental, social, and governance (ESG) factors on European banking corruption. Thus, its novelty is based on considering anti-competitive concerns as a major component that may considerably impact fraud and bribery in corruption investigations. (2) Methods: To approach the research question, we conducted an examination of anti-competitive practices at 344 financial institutions headquartered in Europe throughout the period 2018 to 2022 using the entropy weight and TOPSIS methods. (3) Results: This study reveals that anti-competitive actions are typified by environmental debate and genuine policy competition. Analysing the results prompted us to reach this conclusion. The present study’s findings reveal that financial institutions in Scandinavian nations demonstrate the most significant anti-competitive activity. (4) Conclusions: This research is the first study to underscore the concept of anti-competition disputes and their impact on the emergence of corruption, extortion, and fraud in the European banking sector. Although anti-competitive and corrupt practices may appear to be distinct concepts, they both lead to the financial sector acquiring disproportionate control over the market.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".