Ukraine’s commitments under Association Agreement: challenges and opportunities for the steel industry
Bibliographic record
Abstract
\nThe steel sector stands for a quarter of Ukraine’s industrial gross value added and is a backbone of the country’s economy. However, owing to lastingly insufficient investments to modernisation, the industry is largely obsolete: 70–80% of the production facilities are in operation beyond their final designed term of exploitation. Technology backwardness, coupled with excessive iron ore mining, results in an enormous environmental footprint. Owing to the domestic political and socioeconomic factors and severe competition on the global scale, the steel output hit its historic low in 2017. Recently, the EU became major Ukraine’s trade partner with steel export share of 32% in 2017. Modalities of this partnership will be gradually shaped in context of the EU-Ukraine Association Agreement (entered into force since 01.09.2017), which stipulates transposition into Ukrainian law of some European directives, potentially sensitive for the iron and steel sector. In this paper, the current state of Ukraine’s steel industry was analysed, focusing competitiveness and environmental impact. The analysis performed reveals that short-term implications of the Association Agreement may expose the Ukrainian steelmakers to additional costs; however, the need to comply with the EU regulations is seen as an important factor, motivating the steel industry to modernise and, in the long-term, improve its economic performance and reinforce competitiveness.\n
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".