AJI volume 117 issue 4 Cover and Front matter
Bibliographic record
Abstract
Asset recovery is a fundamental principle of anti-corruption law, without which the financial damage from corruption cannot be repaired.Yet recovering assets is notoriously difficult and time-consuming, and the United Nations Convention Against Corruption provides little technical or institutional support to facilitate such returns.To remedy this, we propose the creation of a transnational asset recovery mechanism that could provide myriad services to states on upon request, including gathering and publishing information, providing technical assistance and capacity-building, helping to conclude agreements on asset return, and monitoring returned funds.Theoretically, we introduce the concepts of customizability and selectability to explain why a flexible transnational asset recovery mechanism has advantages over more formal international institutions, such as an international anti-corruption court.These benefits include lower financial and political costs, enhanced adaptability, and a greater likelihood of enhancing interstate cooperation regarding asset returns. Revisiting Coercion as an Element of Prohibited Intervention in International Law Marko Milanovic 601International law prohibits states from intervening in the internal and external affairs of other states, but only if the method of intervention is coercive.This Article argues that coercion can be understood in two different ways or models.First, as coercion-as-extortion, a demand coupled with a threat of harm or the infliction of harm, done to extract some kind of concession from the victim state-in other words, an act targeting the victim state's will or decision-making calculus.Second, as coercion-as-control, an action materially depriving the victim state of its ability to control its sovereign choices.This may be done even through acts like cyber operations that the victim state is entirely unaware of.The Article argues that many of the difficulties surrounding the notion of coercion arise as a consequence of failing to distinguish between these two different models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.738 | 0.613 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".