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Record W4400198147 · doi:10.19165/2022.3.01

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

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

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
FundersMinistère de la Défense Nationale
KeywordsDroneContext (archaeology)State (computer science)TerrorismSituatedMiddle EastPolitical sciencePublic relationsSociologyComputer scienceArtificial intelligenceLawGeography

Abstract

fetched live from 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.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.223
GPT teacher head0.396
Teacher spread0.173 · 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 designObservational
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

Citations8
Published2022
Admission routes1
Has abstractyes

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