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Record W916795267

민간군사기업제도의 도입방안에 관한 연구

2004· article· ko· W916795267 on OpenAlexaboutno aff
김두현

Bibliographic record

Venue시큐리티 연구 · 2004
Typearticle
Languageko
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCivil–military relationsMilitary sociologyMilitary terminologyGovernment (linguistics)Military scienceGuard (computer science)Military personnelCivil affairsPolitical scienceMilitary threatMilitary theoryEngineeringPublic administrationManagementMilitary operations other than warSpanish Civil WarLawPoliticsComputer scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

This paper is to introduce the foreign civil military enterprise system and to suggest a plan to improve the employment rate of the discharged soldiers who served for a long term and the welfare of soldiers highly. At first, Military Professional Resources Inc., Dyncorp Inc. and Cubic Inc. of the United States, Sandline International Inc. of England and ATCO Frontec Inc. and Ednoonton Inc. of Canada are introduced in this paper. And also, this paper is to analyze employment conditions and difficulties of the discharged soldiers and suggest to establish a institute of tentatively named 'Korean Civil Military Enterprise' in terms of the Government and the Department of Defense. This civil military enterprise is to plan and manage military projects, to research and develop drill manuals and military doctrines and policies and to evaluate and manage the military training. As a pattern of civil military enterprises, three types of Military Support, Military Consulting and Military Provider are presented. Moreover, in order to advance the civil military enterprise, diverse training programs(for public civil investigators, guard advisors, special security guards and security staffs) to utilize all sorts of special military fields are developed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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.

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
Published2004
Admission routes1
Has abstractyes

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