Bibliographic record
Abstract
Corruption is not a new phenomenon and it might date back to the humans' beginning of life on earth or their commencement of living in societies.No country could be found in any epoch of the history not afflicted with a sort of corruption; of course, such a cancerous tumor has been and is being more expanded in the third world and developing countries.In Iran, as well, following the termination of the imposed war, and from the turn of 1970s, terms like embezzlement and financial corruption, with a value of 123 billion TOMANS, joined the literature domain of the country and, unfortunately, it ascended to a higher value in 1980s and finally peaked in 1990s.Meanwhile performing a pathological study of economic corruption's mushrooming, the present study analyzes the rules of fighting it and their contingent shortcomings and the required regulations pertinent to fight against corruption in legal terms in such a way that the punishments of the economic corrupts and the rules of fighting against corruption could be executed so that no corrupt can find a way of escaping the legal punishment and the branches enforcing the legal regulations could better fight economic corruption.Therefore, it is through clarification of economic regulations, observation of meritocracy, elimination of the unnecessary rules and regulations, improvement of the existing rules and decisive confrontation with the economic criminals with no political and factional considerations that, besides preventing the economic corruption from being dispersed, the social justice can be institutionalized in the society more than ever before.
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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 | 0.000 | 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 | 0.000 | 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) | 0.999 | 0.996 |
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".