Bibliographic record
Abstract
This article studies the politics of regional cooperation and regionalism, with particular attention to “good neighbourly relations”, which has emerged as a new concept in the field. Since the early 1990s, the Balkan countries have pursued attempts to link with each other in regional platforms and networks. Applying the concept of “good neighbourly relations”, this article seeks to reveal how the countries of the region have pursued regional cooperation while facing the reality of persistent bilateral disputes among themselves. While influential studies point to a growing cleavage among neighboring countries, this article seeks to further the understanding of regional cooperation with an assessment of the emergence and rise of the concept of “good neighbourly relations” to a norm of regionalism. By applying a social constructivist perspective this article investigates whether, and how, “good neighbourly relations” functioned as a channel of diffusion of norms pertaining to regionalism and regional cooperation in the Balkans. The article suggests that “good neighbourly relations” provided an opportunity for norm diffusion. By allowing the emerging norm of “good neighbourly relations” to guide regionalism efforts, the ambitions to promote the norm regionally as well as globally were supported.
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 | 0.027 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".