Bibliographic record
Abstract
The article pays tribute to Gabrielle Marceau’s vision of law as a tool for social progress, while examining the opposite dynamic: the regression of international law. It begins by distinguishing between the words “evolve” and “devolve,” showing how erosion can take institutional and linguistic forms. Institutional erosion is illustrated by the paralysis of the World Trade Organisation (WTO) Appellate Body and attacks on the International Criminal Court (ICC), both of which undermine multilateral dispute settlement and justice. Language erosion is explored through two strands: active efforts to dilute legal standards, as seen in humanitarian law debates on cyber operations or in the United Nations Security Council negotiations, and passive erosion, where permissive interpretations of the right to self-defence gradually reshape the norm. The article highlights how these erosions may be driven by states that feel international law no longer serves their political interests, though they carry the risk of fragmentation and unpredictability. At the same time, it notes forms of resistance and adaptation, such as the WTO’s Multi-Party Interim Appeal Arrangement, the Kampala Amendments on the Crime of Aggression to the Rome Statute of the ICC , and initiatives by states to defend humanitarian law. The overall argument is that vigilance and proactive engagement are needed to ensure that international law’s flexibility, central to Gabrielle Marceau’s legacy, continues to serve justice and humanity rather than regression.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 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".