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Record W4232750727 · doi:10.1017/ajil.2020.44

Trump Administration Authorizes U.S. Military Use of Non-persistent Landmines

2020· article· en· W4232750727 on OpenAlexaboutno aff

Bibliographic record

VenueAmerican Journal of International Law · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicNuclear Issues and Defense
Canadian institutionsnot available
Fundersnot available
KeywordsAdministration (probate law)TreatyPolitical scienceConventionMember statesPeninsulaLawInternational tradePublic administrationBusinessGeographyEuropean unionArchaeology

Abstract

fetched live from OpenAlex

The Trump administration released an updated policy on landmine use on January 31, 2020, significantly changing the prior U.S. policy. Whereas previously the United States supported landmine use only on the Korean peninsula, the revised policy now allows for the universal use of “non-persistent” landmines, i.e., those that have self-destruction mechanisms and self-deactivation features. This policy significantly diverges from provisions of the Convention on the Prohibition of the Use, Stockpiling, Production and Transfer of Anti-Personnel Mines and on Their Destruction (Ottawa Convention), a treaty to which the United States is not a party but which has widespread international support.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0060.002
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0200.007

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.

Opus teacher head0.032
GPT teacher head0.310
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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