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.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.949 | 0.959 |
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; the direct Gemma label and the distilled Codex classifier 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".