Bibliographic record
Abstract
The 'Four Societies' conferences are a collaborative initiative of the American Society of International Law ('ASIL'), the Australian New Zealand Society of International Law ('ANZSIL'), the Canadian Council on International Law ('CCIL') and the Japanese Society of International Law ('JSIL').The biannual conferences, which began in 2006, provide an opportunity for emerging scholars to foster a collaborative international network around a common theme.Previous themes have included changing actors in international law,1 global environmental change,2 experts and networks,3 disaster relief,4 globalisation5 and democratic theory and the law.6The theme for the 2022 conference, which was originally to be hosted by the University of California, Berkley School of Law in 2020, but which was postponed to 2022 and held online owing to COVID-19, was 'Areas Beyond National Jurisdiction' .The papers presented at the conference addressed the theme from a range of perspectives: that the oceans, polar regions, outer space and cyberspace are places where international law is evolving and innovating; that evolving technological capabilities and burgeoning human population are pushing the reach of human activities into these spaces and this is demanding a response in multiple legal fields such as environmental, national security, communications, navigation, economic intellectual property, criminal and 1
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.512 | 0.406 |
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".