ВНЕШНЯЯ ПОМОЩЬ РАЗВИТИЮ УКРАИНЫ ПОСЛЕ 2014 Г.: МАСШТАБЫ, ПРОЕКТЫ И МОТИВАЦИЯ ДОНОРОВ
Bibliographic record
Abstract
The article deals with assistance aid provided by the international donors to Ukraine. Author analyzes Ukrainian statistics on the issue – the projects registered between January 2014 and February 2018 by two Ukrainian ministries – the Ministry of Economic Development and Trade and the Ministry of Finance. Although incomplete, this data is considered assistance, which has reached Ukraine. The author names the overall volumes of international assistance to Ukraine, amounts offered in loans and grants and the major allocations of assistance. Proceeding from priority areas of aid, the author concludes on the donor’s motivations and their possible specific interests in Ukraine. Major Ukrainian donors – international financial organizations (IMF, IBRD, EIB, EBRD and KfW), as well as the European Union, the UN, Chernobyl Shelter Fund and donor states (the United States, Germany and Canada) have specific approaches towards assistance aid. While multilateral institutions tend to address the needs of Ukrainian economy by funding the reforms and infrastructure, donor states pay more attention to their long-term strategic and economic interests. They fund nuclear security and non-proliferation, support defense, law enforcement and border control agencies, encourage civil society and media development, consult agricultural sector and bilateral trade. States also ensure that national companies become contractors of their aid projects. Common motivation both for multilateral donors and states is to turn Ukraine into a western-like state with a transparent system of governance sensitive to foreign influence.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.135 |
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".