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Record W3168907618 · doi:10.21810/jicw.v4i1.2749

Jurisdictional Challenges in the 21st Century Security Environment

2021· article· en· W3168907618 on OpenAlexaffvenue
William J. McAuley

Bibliographic record

VenueThe Journal of Intelligence Conflict and Warfare · 2021
Typearticle
Languageen
FieldEngineering
TopicMilitary Strategy and Technology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSecurity studiesArchitecturePolitical scienceCorporate securityCritical security studiesEconomic securityBusinessComputer securitySecurity servicePublic administrationNetwork security policyPublic relationsInformation securityComputer scienceLawGeographyCorporate social responsibility

Abstract

fetched live from OpenAlex

What security and intelligence strategies do subnational governments require to protect themselves and the social, cultural, economic, and safety interests of their citizens? Although subnational governments wield important levers in areas now inhabited by an expanding array of domestic and foreign threat actors, few have any coherent security and intelligence culture, architecture, or strategy. This paper seeks to address the conspicuous absence of discourse on contemporary subnational security challenges, suggesting that subnational security strategies are an inescapable requirement of the 21st century security environment. Given that the inception of any form of polycentric security strategy with an enabling architecture and culture is a complex undertaking, the utility of the design-basis threat concept is explored to provide a tangible starting point for evaluation of key drivers for analysis in the contemporary subnational security environment. A simplified framework for a provincial-level design-basis threat analysis is proposed as a gateway to deeper analysis.

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)0.007
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.040
Scholarly communication0.0160.011
Open science0.0010.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.233
Teacher spread0.203 · 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 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

Citations2
Published2021
Admission routes2
Has abstractyes

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