La médiation des petits États : rétrospective et perspective (Note)
Bibliographic record
Abstract
Propped by Norway's diplomatic breakthrough in getting Israel and the PLO to forego their protracted state of war and negotiate their way toward a peaceful coexistence, this study focuses on small state mediation. It is an intellectual endeavor to shed light on a small state intervening as a third party mediator in some conflict-ridden international disputes. Because of the normative and legalistic approach that has dominated the study of international relations for quite some time, it is not surprising that the process of mediation can be difficult to grasp or, at least, subject to misunderstanding. Highlighting the small state mediation paradigm, the essay sets out Us background and offers an interpretative analysis of Us value. It addresses a number of questions pertaining to this avenue for international conflict management. For instance, where does international mediation fit within the larger spectrum of international peace mechanisms ? What are the distinguishing features of a small state's style of intervention, and how does a small state mediation differ from other mediatory schemes such as those intiated by other actors ? Most importantly, where does a small state's wherewithal to bring about a peaceful settlement of an international dispute stem from ? These and other questions are carefully probed in an attempt to grasp the dynamic of small state mediation.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".