Bibliographic record
Abstract
Large language models (LLMs) are currently disrupting law. Yet their precise impact on international law, especially treaty interpretation, remains underexplored. Treaty interpretation can be analogised to a game in which ‘players’ strategically deploy ‘cards’, usually principles of treaty interpretation, to persuade an ‘audience’ that their interpretation is correct. Leveraging this analogy, this paper offers a limited case study of how OpenAI’s ChatGPT, a prominent LLM-based chatbot, navigates the treaty interpretation game. In line with the existing research on ChatGPT’s legal abilities, the author concludes that ChatGPT competently plays the treaty interpretation game. This conclusion leads to a broader discussion of how LLM usage may impact international law’s development. The argument advanced is that, while LLMs have the potential to enhance efficiency and accessibility, biased training data and interpretative standardisation could reinforce international law’s dominant narratives. As such, this paper concludes with a cautionary note: the potential gains derived from LLMs risk being offset by disciplinary stagnation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".