Страхове шахрайство в сучасних умовах функціонування страхових ринків \n(Insurance fraud in modern conditions of insurance market)
Bibliographic record
Abstract
У статті досліджено сутність страхового шахрайства в Україні та інших країнах світу, зокрема Канади, детально описано основні складові (мотиви) страхового шахрайства, виявлено ряд проблем, з якими стикається Україна в процесі боротьби з шахрайством на страховому ринку, а також описано методи подолання цього явища. У висновку доведено необхідність консолідації зусиль держави, страхових компаній та їх об’єднань в плані \nборотьби із проявами страхового шахрайства в Україні. \n(This article explores the essence of insurance fraud in Ukraine and other countries, including Canada, details the main components (causes) of insurance fraud, revealed a number of problems faced by Ukraine in terms of dealing with fraudlance in the insurance industry including methods of dealing with it.. In conclusion, the necessity of consolidating efforts of the government, insurance companies and their associations in terms of Fight against insurance fraud in Ukraine has been proved.)
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".