Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".