The Law of Armed Conflict as Soft Power: Optimizing Strategic Choice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
is distinguished by non~participation in various core legal regimes gov~ erning armed conflict.Perhaps most significant is its continued refusal to ratify the 1977 Additional Protocol I to the four Geneva Conventions of 1949, an in~ strument that most States consider the linchpin of this body oflaw. 1 Today, the United States is one of only three NATO countries which is not Party to the Protocol, and of the remaining two, France and Turkey, the former is expected to ratify the agreement in the near future.The United States also rejected the 1997 Ottawa Treaty, which prohibits the use, stockpiling, production, or trans~ fer of anti~personnel mines. 2 By March 1999, over 135 States had signed or ac~ ceded to the treaty, including every NATO ally except Turkey.More recently still, in 1998, the United States refused to sign the Statute of the International Criminal Court (ICC) ,3 a constitutive instrument for the first permanent inter~ national tribunal to handle genocide, crimes against humanity, war crimes, and crimes of aggression.Of the countries represented at the Rome Conference, where the final drafting of the Statute occurred, only seven voted against the treaty.Joining the United States in opposition were China, Iraq, Israel, Libya, Qatar, and Yemen, hardly an admirable grouping ofbedfellows. 4Such exam~ pIes are illustrative, not exhaustive.Over the past half decade, the United
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it