Problems of Detecting Economic Crimes in Ukraine (1996–2021)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Criminological investigation of correlation between corruption and organized economic crime testifies that organized crime and corruption first of all endangers national security of Ukraine, its further development, ensuring constitutional system, proper functioning of all political-economic system. That is why not accidentally the Decrees of the President of Ukraine “On Complex Earmarked for a Specific Purpose Program for Fighting Criminality” and “On Complex Program in Preventing Criminality” define fighting organized crime and corruption in an economic sphere as one of priority directions. Detecting of organized crimes committed under not obvious circumstances is a complicated and multifactored. The necessity of quick and correct solving informational, methodological, tactical, psychological, technical and many other issues predetermined active participation of inquiry and search workers, the Security Service, militia, its operative detachments, the Procurator’s Office, experts, professionals, and the public. That is why a complex approach to fighting crime, including economic one, needs further development, also improvement in co-operation between all law-enforcement and controlling organs is needed. The work in this direction should be considered one of priorities for state organs nowadays and in future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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 it