Bibliographic record
Abstract
The present recurrence of mass atrocity crimes, though shocking, is nothing new. With conflict raging in the Democratic Republic of Congo, Myanmar, Sudan, and elsewhere, why have actors or coalitions outside the authority of the United Nations not emerged to exercise a credible deterrent and halt the bloodshed? Much of the answer lies within the framework of international law. This article seeks to understand whether the law serves as constrainer or enabler of unauthorised humanitarian interventions. I argue that the integrity of international law is best preserved by maintaining the illegality of unauthorised, militarily-coercive interventions. Simultaneously I posit that states should take the coercive actions necessary to end large-scale killing and that their actions should be considered and authorised, ex post facto, by the Security Council based on contextual “exceptionality.”
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".