The Unraveling of Canada's Legal Justification for Force in Syria? The Trouble with "Unwilling and Unable"
Bibliographic record
Abstract
[quote]\nAs summarized by Jennifer Daskal over at Just Security:\nU.S.-led forces hit a convoy carrying pro-Syrian government forces advancing inside a deconfliction zone inside Syria. The convoy was reportedly traveling toward the al-Tanf military base used by U.S. coalition forces to train anti-ISIS fighters. U.S. and coalition officials assert that the Russians “apparently” attempted to dissuade the convoy from entering the area, that they first fired warning shots and deployed two US aircraft as a show of force, and only struck the convoy after it failed to heed the warning, as a means of protecting U.S. and coalition forces.\nJennifer Daskal also reports US official statements that the “strike was a proportionate response done for purposes of force protection—an act of self-defense in an effort to protect U.S. forces.” She observes: “This is, on its face, quite plausible. And, if accurate, lawful as a matter of both international and domestic law.”
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.017 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| 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".