“Rolling Up the Sleeves”1 How EU policy towards Serbia and Montenegro acts as the glue that holds the State Union together?
Bibliographic record
Abstract
The most powerful tool of EU foreign policy in dealing with potential candidate countries (and beyond) is that of political conditionality. The successes of this policy, as well as its spectacular failures, have been largely well documented by the political science research community. Far less research, however, goes into explaining the scenarios where the EU goes “beyond conditionality” (Teokarevic 2003) in its dealings with potential candidates for membership in the EU. The goal of this paper is to explain the extremely intensive and pro-active EU involvement in the drafting of the Constitution of Serbia and Montenegro and the subsequent attempts by Brussels to determine the future nature of the union between these neighboring republics. In answering this question the paper looks at the history of EU’s involvement in the region and attempts to provide a theoretical framework that can best provide the explanation for the motivation of EU’s policy makers to utilize such a direct strategy of involvement that goes far “beyond conditionality”. Full text available: https://doi.org/10.22215/rera.v2i2.170
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".