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Record W2992499528

Review of Psychological Study on Crisis Negotiation

2014· article· en· W2992499528 on OpenAlexvenueno aff
Liu Jianqing

Bibliographic record

VenueCross-cultural communication · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsnot available
Fundersnot available
KeywordsPersonality psychologyNegotiationPsychologyCrisis interventionSocial psychologyPersonalityLaw enforcementSubconsciousPublic relationsPolitical scienceLawMedicine
DOInot available

Abstract

fetched live from OpenAlex

The crisis negotiation is a law enforcement action to remove the threat of hijackers and rescue hostages in a peaceful way. The essence of successful crisis negotiation is effective communication. About forty years of practical experience and empirical research on crisis intervention show that the way of communication is better than that of the tactical attack; the personality traits of a negotiator, such as emotional stability, empathic ability, divergent thinking mode, etc., have an important influence; the hijackers are characterized with emotionality, criminality and spirituality in diversified motivations, and paranoid type and abnormalism in personality and behavioral characteristics; the hostages may appear specific subconscious self-defense reaction of Stockholm syndrome during the crisis; and the successful negotiator shall be the combination of police and clinical psychologist with rich practical experience, and be the “psychologist with gun” with an effective confrontation to the hijackers and crisis events. Based on early practice and theoretical model of crisis negotiation, this article focuses on reviewing and analyzing the communication techniques such as active listening, self-exposure and role playing in crisis negotiation, characteristics and psychological reactions of hijackers and hostages, and personalities and behavioral characteristics of negotiators and selection and training programs of negotiators.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.080
GPT teacher head0.469
Teacher spread0.389 · 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 designNot applicable
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

Citations5
Published2014
Admission routes1
Has abstractyes

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