Bibliographic record
Abstract
This article discusses the conflicting use of nonstate actors in state-sponsored actions.It also introduces a diplomatic strategy for regulating the application of violence by private military and security companies.O n the night of February 7-8, 2018, for the first time since the Vietnam War, American and Russian forces clashed directly.1 A Russian-Syrian force of approximately 500 fighters crossed the Euphrates River near the eastern Syrian city of Deir ez-Zzor and launched an attack.The target, on the other side of the river, was a base for the Kurdish Syrian Democratic Forces and its US military advisors.During the three-hour battle that followed, the US military deployed artillery, jets, helicopters, and unmanned aerial vehicles.In the subsequent press conference, Lieutenant General Jeffrey L. Harrigian, the commander of US Air Forces Central Command, reported these US forces "release[d] multiple precision fire munitions and conduct[ed] strafing runs against the advancing aggressor force, stopping their advance and destroying multiple artillery pieces and tanks." 2 While US forces incurred no casualties, some reports suggest as many as 100-200 Russians were killed in the engagement.3 Adding to the significance and complexity of this event, the Russian forces were not soldiers in state uniforms.Instead, they were personnel of Wagner, a Russian private military and security company (PMSC).In recent years, 2,500 Wagner personnel have operated in Syria as Russia's unofficial "boots on the ground."4 Reports of the company using a military base in southern Russia and relying upon state-sponsored military logistics and medical services tie the company to Russian state actors.5 Nevertheless, officials responding to the February battle could simply distance themselves: "Russian service members did not take part in any capacity and Russian military equipment was not used."6 Elements in the nation's media drew a further distinction: "It was a 1 Joshua Yaffa, "Putin's Shadow Army Suffers a Setback in Syria," New Yorker, February 16, 2018.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".