Development of the Republic of Sakha (Yakutia)'s Shadow Economy Assessment Methodology
Bibliographic record
Abstract
The purpose of the article is to study the shadow economy assessment methodology. This article presents a comprehensive study of the parameters of a shadow economy, considers its essence, and defines its terminology. This study outlines the historical approach to the development of the shadow economy, both in Russia and worldwide, and gives a brief analysis of the economy of the Republic of Sakha. The authors examined the specifics of the statistical methods applied in assessing various structural elements of a shadow economy and measured and assessed the shadow economy in this region. The research conducted enabled the authors to formulate the main measures required to reduce the shadow economy. The scientific novelty is justified by the research results, which included studying and summarizing a wide range of published and unpublished materials, the examination of the initial and transitional periods of the shadow economy development in Yakutia. The article reveals the main causes and conditions that lead to the formation of the shadow economy in various sectors of the Yakutia economy. The solutions and suggestions proposed in the article are aimed at reducing the shadow economy parameters. The scientific research results are of theoretical and applied importance for public administration and authorities to improve the effectiveness of the fight against the shadow economy manifestations.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".