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Record W3024648065 · doi:10.1080/25751654.2020.1760021

Command and Control of India’s Nuclear Arsenal

2020· article· en· W3024648065 on OpenAlexaff
Lauren Borja, M. V. Ramana

Bibliographic record

VenueJournal for Peace and Nuclear Disarmament · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicNuclear Issues and Defense
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCommand and controlNuclear weaponDoctrineArms controlControl (management)Nuclear ethicsPolitical scienceComputer securityEngineeringLawComputer scienceManagementTelecommunicationsEconomics

Abstract

fetched live from OpenAlex

Despite long-standing debate about the challenges of establishing command and control of India’s nuclear weapons, few details about the structure and organization of such a system exist in the public domain. Objectives for effective command and control have been laid out in India’s Draft Nuclear Doctrine of 1999, which was followed by the more official statement from 2003 that described some of the organizations governing the new arsenal. It is now almost twenty years later, and many changes have occurred within Indian nuclear force structure. This article documents these evolutions and details some of the similarities and differences between how nuclear weapons might be controlled in India as compared to states that developed nuclear weapons earlier. It specifically examines some of the relevant infrastructure and capabilities, such as military command centres, satellites, and delivery vehicles, that have been developed in the last two decades that are important to nuclear command and control. This article also identifies continuing challenges, such as risks due to the entanglement of conventional and civilian infrastructure with nuclear systems, associated with command and control of nuclear weapons in India.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.027
GPT teacher head0.298
Teacher spread0.270 · 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 designQualitative
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

Citations8
Published2020
Admission routes1
Has abstractyes

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