Bibliographic record
Abstract
At least thirty percent of Ukrainians enter colleges by paying bribes while many others use their connections with the faculty and administration. Corruption increases inequalities in access to higher education, prevents future economic growth in the country, and undermines quality and credentials of academic degrees. This paper considers corruption in higher education in Ukraine, including such aspects as corruption in admissions to higher education institutions and corruption in administering the newly introduced standardized test. The reform of higher education in Ukraine, based on the national examinations, is intended to be a response to the rapidly changing economic environment and the new social order. Au moins un tiers des étudiants ukrainiens des collèges universitaires a été admis en payant des pots-de-vins, le reste s’est servi de ses contacts avec les départements académiques et administratifs. La corruption accentue un accès inégal aux universités, freine la croissance économique future du pays et remet en question la qualité et les cartes de présentation des diplômes académiques. Cet article vise la corruption dans les universités ukrainiennes et plus précisément, la corruption lors des processus d’admission dans les institutions d’éducation supérieure ainsi que dans l’administration du nouvel examen standard utilisé à cet effet. La réforme de l’éducation supérieure en Ukraine, basée sur l’application d’examens nationaux, cherche à répondre à un nouvel ordre économique et surtout à un environnement économique en constante mutation.
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 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".