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Record W3124664873

Nuclear Doctrines and Stable Strategic Relationships: The Case of South Asia

2016· article· en· W3124664873 on OpenAlexaff
Mahesh Shankar, T. V. Paul

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicNuclear Issues and Defense
Canadian institutionsMcGill University
Fundersnot available
KeywordsOperationalizationNuclear weaponDoctrineNuclear ethicsPolitical scienceDeterrence theoryCLARITYNuclear proliferationSouth asiaLaw and economicsPolitical economyDevelopment economicsLawSociologyEconomicsEpistemology
DOInot available

Abstract

fetched live from OpenAlex

This article offers a discussion of nuclear doctrines and their significance for war, peace and stability between nuclear-armed states. The cases of India and Pakistan are analysed to show the challenges these states have faced in articulating and implementing a proper nuclear doctrine, and the implications of this for nuclear stability in the region. We argue that both the Indian and Pakistani doctrines and postures are problematic from a regional security perspective because they are either ambiguous about how to address crucial deterrence related issues, and/or demonstrate a severe mismatch between the security problems and goals they are designed to deal with, and the doctrines that conceptualize and operationalize the role of nuclear weapons in grand strategy. Consequently, as both India's and Pakistan's nuclear doctrines and postures evolve, the risks of a spiraling nuclear arms race in the subcontinent are likely to increase without a reassessment of doctrinal issues in New Delhi and Islamabad. A case is made for more clarity and less ambition from both sides in reconceptualizing their nuclear doctrines. We conclude, however, that owing to the contrasting barriers to doctrinal reorientation in each country, the likelihood of such changes being made — and the ease with which they can be made — is greater in India than in Pakistan.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.030
GPT teacher head0.280
Teacher spread0.250 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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