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Record W2952958752 · doi:10.22215/etd/2016-11578

Nuclear “Pork” Revisited: Organizational Imperatives and Nuclear Weapon Program Abandonment

2016· dissertation· en· W2952958752 on OpenAlexaff
Simon Palamar

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsCarleton University
Fundersnot available
KeywordsAbandonment (legal)BureaucracyNuclear weaponNuclear ethicsPolitical scienceIncentiveArms controlPolitical economyPublic administrationEconomicsLawPoliticsMarket economy

Abstract

fetched live from OpenAlex

Since 1941 at least 30 countries have conducted nuclear weapon activities.Over twothirds of those countries have abandoned their nuclear weapon activities as a matter of policy, making nuclear abandonment the dominant historical tendency.Understanding why countries abandon their nuclear weapon activities holds the promise of allowing policy makers to improve efforts to control the spread of nuclear arms around the world.This dissertation offers a novel explanation for nuclear abandonment.It draws from the literature about bureaucracy and foreign policy to argue that the narrow imperatives of the organizations that conduct nuclear weapon activities can actually make nuclear weapon program abandonment more -rather than less -likely.This stands in stark contrast to the conventional wisdom about bureaucratic imperatives and nuclear weapons, which is that bureaucratic organizations, such as nuclear science agencies and militaries, push governments to acquire nuclear arms.In contrast to that prevailing wisdom, this thesis explains why some of the organizations that governments have charged with executing national nuclear weapon policies face weak incentives to act as nuclear bomb lobbyists.Thus, bureaucratic or organizational imperatives do not only work in favour of nuclear proliferation.They can also work in favour of nuclear abandonment by contributing to a policy environment that makes nuclear abandonment more likely.While there are many explanations for why countries abandon their nuclear weapon activities, most explanations are only substantiated by small, non-random data samples.This dissertation offers a generalizable and probabilistic explanation for nuclear abandonment, and substantiates its theoretical claims with large-N multivariate regressions that use an original data set that covers approximately 670 country-year observations and that exploits natural variation in the types of organizations that have conducted nuclear weapon activities.The large-N empirics are supplemented with two case studies of nuclear weapon policy in Switzerland (1945-1988) and South Africa (1969-1993).iii

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)0.019
metaresearch head score (Gemma)0.062
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: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.062
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.016
Scholarly communication0.0050.009
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.313
Teacher spread0.306 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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