The Genesis of Opposition and Responsibility for Financial Abuses in Foreign Economic Activity
Bibliographic record
Abstract
These recommendations are part of what is obviously a much broader and more comprehensive agenda for the continued strengthening of efforts to combat organized crime and corruption in Ukraine. Nevertheless, they represent a carefully considered set of priorities that promise to overcome continued deficiencies while also building on the progress that Ukraine has already made in what will continue to be an enormously tough endeavor. It is as well to keep in mind that the fight against organized crime is typically characterized as much by setbacks as by success, as much by failure and disappointment as triumph and acclaim. Organized crime is like a constantly mutating virus DOI: 10.32370/IA_2023_12_3 that out-maneuvers all efforts to destroy it. If it can be isolated and quarantined and then harm can be limited. Yet even this modest objective is one that is enormously difficult to attain. The initiatives suggested here are certainly no guarantee of success even in harm reduction. Yet without such measures, the only certainty is failure - and the eventual emergence of Ukraine as a state and economy captured and dominated by organized crime. The implementation of a strategy that is comprehensive in scope while highly selective in its targets and priorities offers at least some hope that such an outcome can be avoided.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".