Bibliographic record
Abstract
The most outspread kind of corruption according to the experience of private entrepreneurs, is in the acquisition of a building location, then during acquisition of import-export licenses, acquiring of government contracts, and acquisition of telephone and electric power services. In contrast to this, bribery is present the least in the fields of tax administration, company registration, and approval of new sales prices Outspread of corruption is large. More than a half of private entrepreneurs (57,8%) have declared that they have paid to the public officials so called additional services. Contrary to this, one quarter (24,8%) declared that have not done something like that. Most frequent amount of this additional payment goes between 1 and 10% of their income. They not denied that sometimes is possible to avoid this payment, at the cost of spending large amount of time. There is also no guarantee that paid 'service' will be consumed for sure, so one third and often one fifth of entrepreneurs are in need to pay same service again. Service that is matter of deal can be obtain: always in 8,3% cases, usually in 40,1%, often in 15,0%, sometimes in 11,9%, rare in 3,4% and never in 5% of cases.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".