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Record W4280639190 · doi:10.1177/00207020221100712

How to de-escalate dangerous nuclear weapons and force deployments in Europe

2022· article· en· W4280639190 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicNuclear Issues and Defense
Canadian institutionsWestern University
Fundersnot available
KeywordsArms controlNuclear weaponNegotiationOperationalizationTreatyDisarmamentInternational tradePolitical scienceNorth Atlantic TreatyCold warLawPolitical economyComputer securityLaw and economicsBusinessSociologyComputer sciencePolitics

Abstract

fetched live from OpenAlex

Amidst the war in Ukraine, it is important to raise the prospect and vision of creating mutual security guarantees and ridding Europe of its dangerous nuclear weapon systems and provocative force deployments. In view of reckless Kremlin rhetoric and aggressive military action in Russia’s so-called near abroad, it is time for renewed approaches to arms control. As the Ukraine situation plays out, Russia, the United States, and allies in the North Atlantic Treaty Organization must return to their bargaining tables and negotiate strict limits, verification measures, and overarching controls over their nuclear use doctrines, weapon stockpiles, and conventional force deployments. All sides will have to make deep concessions and de-alert and de-operationalize mid- and short-range nuclear weapons while improving command and control safeguards—because, as we see, brandishing weapons and threatening escalation heightens tensions and increases the danger of crises spiralling uncontrollably.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.302
Teacher spread0.292 · 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