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Record W4412736214 · doi:10.1108/jacpr-04-2025-1003

Hybrid threats and human security: the impact of hybrid war and hybrid interference on civilians

2025· article· en· W4412736214 on OpenAlexaff
Myriam Denov

Bibliographic record

VenueJournal of Aggression Conflict and Peace Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and Russian Geopolitical Military Strategies
Canadian institutionsMcGill University
Fundersnot available
KeywordsHuman securityComputer securityInterference (communication)Political scienceCriminologyPsychologyLawComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Purpose This paper examines the evolving landscape of hybrid warfare and hybrid interference and the impact of these hybrid threats on the lives of civilians. Design/methodology/approach This paper outlines the origins of the concept of hybrid warfare, its strengths and limitations and the push for conceptual refinement, leading to the introduction of the concept of hybrid interference. The key characteristics of hybridity are addressed, particularly in relation to actors and strategies. Key domains of hybrid attacks are explored. Findings Drawing on a human security lens, this paper reveals the profound ways in which the people’s daily lives are affected by hybrid threats in the realms of information, cyber and economics. It highlights the importance of moving beyond macro-level understandings of hybrid threats to include the security of everyday people. Social implications The implications of hybrid threats are examined, alongside key strategies for tackling hybrid threats, including resilience building and a whole-of-society approach to countering hybrid threats. Originality/value While the issue of hybrid threats has been explored extensively, as well as its macro implications, little attention has been paid to micro-level implications. This paper addresses the impact of hybrid threats on the daily lives of civilian populations.

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)0.003
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 score0.361
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.073
GPT teacher head0.442
Teacher spread0.369 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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