Bibliographic record
Abstract
The ethno-territorial conflicts precipitated by the breakup of the Soviet Union and dissolution of Yugoslavia had major implications for European security and substantially altered the strategic priorities of the North Atlantic Treaty Organization (NATO) in the post-Cold War period. In order to preserve regional stability, NATO expanded beyond collective defense into crisis management and peacekeeping to address destabilizing ethnic conflicts, particularly in the Balkans region. This paper proposes that the NATO peacekeeping operation succeeded as a whole in stabilizing the Kosovo crisis and enabled the creation of a functioning, multiethnic state in Kosovo, which became independent in 2008. Using Kosovo as a case study, this paper outlines a conceptual framework for use in examining the success of the NATO peacekeeping force in Kosovo, known as KFOR, in stabilizing the Kosovo crisis and enhancing regional stability. This framework consists of five core security tasks – diffusing the crisis; ensuring security; enabling humanitarian relief operations; facilitating a political solution; and fostering long-term regional stability – organized around an end state of establishing a stable, independent Kosovo. The paper concludes with comments on the usefulness of KFOR as a model for peacekeeping and the long-term use of NATO forces in such peacekeeping operations going forward.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".