The Experience of the United States and Canada in Combating Corruption in the Education System and Opportunities for Uzbekistan
Bibliographic record
Abstract
This article is devoted to the topical issue of improving the fight against corruption in the education system of Uzbekistan. The article’s aim is to analyze the experience accumulated by the USA and Canada in the fight against corruption in the education system in order to study the possibilities of applying the most successful practices of these states in Uzbekistan. The research methods were the following: analysis of scientific literature and legal acts, comparative legal analysis, induction, deduction and forecasting. The article’s author comes to the conclusion that in the United States it is useful for Uzbekistan to adopt the experience of protecting citizens and civil servants when they apply to the competent authorities about corruption manifestations that they become aware of or information about which they need to verify, while simultaneously protecting the leadership of all organizations from deliberate misinformation on the part of persons making relevant statements. In Canada, it is useful for Uzbekistan to learn from the experience of the widespread implementation of ethical codes for educators, however, it is necessary to exclude excessive legislative detailing of ethical codes so that they are not too difficult to understand and do not require an additional serious system of training staff and consulting officials.\n
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".