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Record W2275906911 · doi:10.14288/1.0073732

Non-traditional security in the post-Cold War era : implications of a broadened security agenda for the militaries of Canada and Australia

2013· article· en· W2275906911 on OpenAlexaboutno aff
Leanne Jennifer Smythe

Bibliographic record

VenuecIRcle (University of British Columbia) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary and Defense Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCold warPolitical scienceSecurity studiesInternational securityPost–Cold War eraPublic administrationPoliticsLaw

Abstract

fetched live from OpenAlex

The growing salience of non-traditional security concerns for the post-Cold War national security of western states has led this author to ask: What implications does the post-Cold War proliferation of “security” threats, and therefore, the securitization of non-traditional challenges, hold for the primary security institution of the state, namely, the military? Using the research design of a heuristic case study, this project seeks to answer this question through the methodology of process tracing, relying on document analysis and semi-structured elite interviews for data. This dissertation first argues that the category of “non-traditional” security concerns can be separated into three “types”: (1) Fragile and Failing States, (2) Global Terrorism, and (3) Transnational Political Challenges. Using this framework, the dissertation then examines two cases, which are the national security strategies of Canada and Australia throughout the post-Cold War Era. For each case, the impact of the securitization of non-traditional security concerns is analyzed with relation to defence policy, military doctrine, force structure, and operational outputs. It concludes that both militaries have been significantly impacted by the securitization of non-traditional security concerns during the post-Cold War Era, although the securitization processes and policy outcomes have been different in each case.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.018
GPT teacher head0.211
Teacher spread0.193 · 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 designObservational
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

Citations1
Published2013
Admission routes1
Has abstractyes

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