GAMBLING AND SHADOW ECONOMIES: SOCIO-MORAL DECAY AND FOREIGN POLICY BEHAVIOR (2001-2025)
Bibliographic record
Abstract
Gambling economies and shadow economic structures have emerged as dynamic processes causing fundamental transformations in the social, moral, and political fabric of states during the first quarter of the twenty-first century. This study analyzes the multilayered relationship between the expansion of gambling economies and shadow economic networks, the socio-moral decay they create in the internal structures of states, and foreign policy behaviors within the context of the 2001-2025 period. The fundamental research question examines through which mechanisms and how the expansion of gambling economies and shadow economic networks during this period affects the social and moral structures of states and their foreign policy behaviors. The study's hypothesis posits that as gambling economies and shadow economic networks expand, socio-moral decay within the state accelerates, and as a result of this decay, foreign policy behavior transforms into a structure that is more inconsistent, unpredictable, and based on short-term interest optimization. The research employs qualitative methodology, and its theoretical framework is grounded in realist school, structural functionalism, constructivism, and critical security theories. Findings demonstrate that gambling economies influence foreign policy behavior through fiscal, institutional, and normative mechanisms. The fiscal mechanism constrains the use of foreign policy instruments by reducing state revenues, the institutional mechanism opacifies decision-making processes, and the normative mechanism undermines foreign policy legitimacy by eroding social trust. The original contribution of this study lies in treating gambling economies not merely as an economic phenomenon but as a mechanism that corrodes the moral foundations of state institutions and degrades foreign policy behaviors. Research findings reveal that gambling and shadow economies must now be addressed not only as economic issues but also as social, political, and international security concerns.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".