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Record W2992071544

The Role Of Artificial Intelligence In Facilitating Military Innovation

2019· article· en· W2992071544 on OpenAlexaffvenue
Colin ijebor

Bibliographic record

VenueJournal of military and strategic studies · 2019
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSubject (documents)Military intelligencePolitical scienceMilitary scienceEngineering ethicsKnowledge managementSociologyEngineeringComputer scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Most scholarly research into the concept of military innovation tend to focus on the nature of military innovation and how it comes about, but rarely investigate the reasons why these innovations come about in the first place. Artificial Intelligence (AI), which forms the basis for many new technological advancements today, can compel military innovation. AI does not constitute military innovation, but the technological advancements produced based on AI can inspire senior officers to innovate in their military establishment. This paper seeks to conceptualize military innovation by considering various definitions and schools of thought on the subject and to examine the potential reasons for innovation as identified by Rosen, highlighting the role of AI within each of them.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.029
GPT teacher head0.276
Teacher spread0.247 · 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
Published2019
Admission routes2
Has abstractyes

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