Bibliographic record
Abstract
It is often suggested that to achieve cooperation and handle environmental problems, treaties should use mechanisms such as sticks (negative incentives) and carrots (positive incentives), so that States find it attractive to contribute to the greater good. In this regard, Barret proposes a general theory of international cooperation to provide guidance on how to negotiate more effective agreements, which involves restructuring incentives, by balancing positive (carrots) and negative (sticks) incentives. Whereas positive incentives that reward law-abiding behavior of States and prevent violations of international obligations function as a “carrot,” measures such as penalties or sanctions addressing noncompliance situations work as a “stick.” The 1987 Montreal Protocol is usually cited for having successfully combined those two elements by financially compensating developing countries for the incremental costs of complying with the Protocol and, at the same time, by using the threat of trade restrictions to enforce obligations. Positive incentives can be divided into two major categories: one that involves market-based mechanisms, for example, carbon trading, and one that makes use of nonmarket-based financial resources, such as official development assistance, voluntary contributions from governments, the private sector, and funds under international treaties.
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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 | 0.001 | 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 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".