Preventing the Baxter Paradox: Periodic Treaty Renewal as a Tool for Dynamic International Law
Bibliographic record
Abstract
The Baxter Paradox exposes a structural flaw in international law: the more faithfully states comply with treaties, the harder it becomes to prove that their behaviour reflects customary international law (CIL), because practice is attributed to treaty obligation rather than opinio juris. Over time, this dynamic can fossilise outdated treaty norms into rigid customs that are difficult to change, even when scientific knowledge, technology and global power structures have moved on. This paper introduces a governance mechanism designed to prevent such normative fossilisation: Periodic Treaty Renewal with Adaptive Sunset Clauses (PTR-ASC). Treaties are engineered as “controlled customs” with mandatory seven-year review cycles, evidence-based revision triggers and automatic expiration unless a supermajority of parties renews them. A Treaty Vitality Index (TVI), ranging from 0 to 100, aggregates scientific, economic, technological and political information to guide revision and renewal. Using formalisation of the paradox, comparative examples (Paris Agreement, Montreal Protocol, UNCLOS, WTO) and a simple game-theoretic model, the paper shows how PTR-ASC can keep treaty regimes flexible without sacrificing stability or consent. The result is a more dynamic architecture for international law—one that treats treaties as living instruments rather than potential fossils.
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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 | 0.034 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".