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Enregistrement W4400198147 · doi:10.19165/2022.3.01

A Comparative Study of Non-State Violent Drone use in the Middle East

2022· report· en· W4400198147 sur OpenAlexfundno aff
Yannick Veilleux-Lepage, Emil Archambault

Notice bibliographique

Revuenon disponible
Typereport
Langueen
DomaineSocial Sciences
ThématiqueTerrorism, Counterterrorism, and Political Violence
Établissements canadiensnon disponible
Organismes subventionnairesMinistère de la Défense Nationale
Mots-clésDroneContext (archaeology)State (computer science)TerrorismSituatedMiddle EastPolitical sciencePublic relationsSociologyComputer scienceArtificial intelligenceLawGeography

Résumé

récupéré en direct d'OpenAlex

This report examines the drone programs of five non-state groups operating in the Middle East: Hezbollah, Hamas, the Houthi Movement, Islamic State (IS), and the Kurdish Workers’ Party (PKK). In contrast to other violent non-state actors, these five groups have shown that they are willing to engage in tactical and/or technical innovation in the use of drones, have sustained a long-term engagement with drone technology and demonstrated the capacity to develop drone infrastructure. The development of drone programs by these five different groups is different in terms of timescales, methods, strategies, and tactics. Therefore, the report rejects the notion that all non-state groups’ drone programs follow a similar course of development. Instead, it argues that a terrorist group’s use of drones needs to be situated within the context of that group’s overarching strategic goals. Because of this, we argue that states and militaries that are going up against these groups need to first understand what a specific group hopes to accomplish with drones in order to fully comprehend the specific threat, and secondly understand the specific challenges presented by innovation within drone programs (as opposed to episodic drone use). This report outlines offers a framework for the study of drone innovation which is not limited to these groups, but which could also apply to other groups in the future. It does this by describing five different routes that non-state actors have taken to develop drone technology. This paper has made three important additions to the body of knowledge on this topic through systematic empirical data collection and analysis. First of all, the findings suggest that there is a need to refocus attention away from the most high-profile threat – that of drone-deployed WMDs – and toward the more common and empirically demonstrated methods that groups use when employing drones. We have found no evidence of a non-state group seriously attempting to deliver WMDs by drone. While there are indications that Islamic State (IS) pursued both WMD programs and drone programs in parallel, there is no evidence that they are sought to integrate the two. Security professionals, as such, should focus their attention on the empirically-demonstrated uses of drones by armed non-state groups, and on the plurality of means through which drones can enhance these groups’ activities. Second, scholarship and security planning must concentrate on the particular danger posed by drone programs (as opposed to the occasional use of drones) and the potential for innovation in drone use. When fighting drone programs, nations and armies need to retain a focus on innovation and adaptation, and they must understand how organizations grow tactically, strategically, and technically. Drone development is neither linear nor static. Finally, this report demonstrates that there is no single route of development for the use of drones by non-state entities, nor is there a pattern that these groups want to follow in order to expand their capabilities. Each organization uses drones in a manner that is unique to its own set of logistical, political, and strategic parameters; hence, drone programs need to be positioned within the larger context of the organization’s military means and operations. Therefore, militaries and states that are confronting drone programs need to maintain a holistic approach. While they may draw on existing practices that have had varying degrees of success in countering drone threats and engage in preventive action to mitigate the scope of drone programs, approaches should consider drone programs not only as a distinct, isolated threat, but also as part of broader military operations, strategies, and conflict processes.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,032
Score d'incertitude au seuil0,063

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0030,002
Communication savante0,0020,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,223
Tête enseignante GPT0,396
Écart entre enseignants0,173 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2022
Routes d'admission1
Résumé présentoui

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