Bargaining over the Caspian Sea — The Largest Lake on the Earth
Bibliographic record
Abstract
The Caspian Sea is considered by some to be the largest lake in the world. This multinational water body is the subject of one of the world's most intractable disputes, involving Azerbaijan, Iran, Kazakhstan, Russia and Turkmenistan. The conflict over the legal status of the Caspian Sea emerged after the collapse of the Soviet Union and has not been resolved yet. This paper intends to provide some insights into the conflict and predict the most possible outcomes of the negotiations based on Social Choice rules and Fallback Bargaining procedures. In this study, the five options for resolving the conflict which has been suggested during the negotiations are introduced and discussed. Some well-known social choice rules including Condorcet Choice, Borda Scoring, the Plurality Rule, Median Voting Rule (MVR), Majoritarian Compomise (MC) and Condorcet's Practical Method (CPM) are applied to find the "socially optimal" resolutions of this conflict. Then some different versions of Fallback Bargaining methods which seek minimizing the maximum dissatisfaction of any bargainer are applied to predict the outcome of the negotiations. Finally, the socially optimal resolutions are compared with Fallback Bargaining methods' results and the advantages and disadvantages of each method are discussed.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".