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Record W2480129233 · doi:10.24385/lincoln.24325519

A holistic, risk, and futures based approach to deception: technological convergence and emerging patterns of conflict

2016· dissertation· en· W2480129233 on OpenAlexfundno aff
Iain Reid

Bibliographic record

VenueLincoln Repository (University of Lincoln) · 2016
Typedissertation
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsnot available
FundersUniversity of California, San DiegoUniversity of ColomboUniverza v LjubljaniUniversitas Gadjah MadaUniversidade do PortoUniversité de GenèveUniversidad de ChileUniversity of HaifaMacquarie UniversityUniversity of GhanaAthens University of Economics and BusinessUniversity of MauritiusUniversiti Malaysia SabahUniversität WienTartu ÜlikoolEge ÜniversitesiUniversidad Nacional de San LuisChonnam National UniversityUniversity of CyprusUniversity of CapetownUniversitetet i OsloKlaipedos UniversitetasMcGill UniversityNorth Carolina Central UniversityUniversity of CanterburyYarmouk UniversityJihočeská Univerzita v Českých BudějovicíchTexas Christian UniversityKuwait UniversityUniversidad Nacional de Colombia
KeywordsDeceptionContext (archaeology)Interpersonal communicationPsychologyMindsetSocial psychologyComputer scienceArtificial intelligence

Abstract

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Modern challenges in forensic and security domains require greater insight and flexibility into the ways deception can be identified and responded to. Deception is common across interactions and understanding how mindset, motive and context affects deception is critical. Research has focussed upon how deception manifests in interpersonal interactions and has sought to identify behaviours indicative of truth-telling and deceit. The growth of mediated communication has further increased challenges in ensuring information is credible. Deception in military environments has focussed on planning deception, where approaches have been developed to deceive others, but rarely examined from counter-deception perspectives. To address these challenges this thesis advocates a holistic approach to deception detection, whereby strategies will be tailored to match context. In accordance with an in vivo approach to research, a critical review of literature related to deception and related areas was conducted leading to the initial development of a theoretical holistic model of deception detection comprising a deception framework and an individual differences (deceiver and target) framework. Following model development, validation with Subject Matter Experts (SMEs) was conducted. Explanatory thematic analysis of interviews conducted with SMEs (n=19) led to the development of meta-themes related to the ‘deceiver’, their ‘intent; ‘strategies and tactics’ of deception, ‘interpretation’ by the target and ‘target’ decision-making strengths and vulnerabilities. These findings led to the development of the Holistic Model of Deception, an approach where detection strategies are tailored to match the context of an interaction, whether interpersonal or mediated. Understanding the impact of culture on decision-making in deception detection and in particular the cues used to detect deception in interpersonal and mediated environments is required for understanding human behaviour in a globalised world. Interviews were conducted with Western (n=22) and Eastern (n=16) participants before being subject to explanatory and comparative thematic analysis identified twelve cross-cultural strategies for assessing credibility and one culturally specific strategy used by Western participants. Risk assessment and management techniques have been used to assess risks posed in forensic and security environments; however, such approaches have not been applied to deception detection. The Deception Assessment Real-Time Nexus©2015 and Deception Risk Assessment Technique©2015 were developed as an early warning tool and a Structured Professional Judgement risk assessment and management technique. The Deception Risk Assessment Technique©2015 outlines multiple ways of identifying andmanaging threats posed by deception and is employable across individuals and groups. In developing the futures-based approach to deception detection, reactive, active and proactive approaches to deception were reviewed, followed by an examination of scenario planning utility and methodology from futures and strategic forecasting research. Adopting the qualitative ‘intuitive logics’ methodology ten scenarios were developed of potential future threats involving deception. Risk assessment of two scenarios was conducted to show the value of a risk assessment approach to deception detection and management. In conclusion, this thesis has developed a Holistic Model of Deception, explored the links between interpersonal and mediated strategies for detecting deception, formulated a risk assessment and management approach to deception detection and developed future scenarios of threats involving deception.

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)0.011
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0060.024
Scholarly communication0.0150.024
Open science0.0030.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.020
GPT teacher head0.277
Teacher spread0.257 · 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 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
Published2016
Admission routes1
Has abstractyes

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