{"id":"W1588912065","doi":"10.1007/978-3-540-69132-7_108","title":"Using an Emotional Intelligent Agent to Reduce Resistance to Change","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Terrorism, Counterterrorism, and Political Violence","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Intelligent agent; Multi-agent system; Human–computer interaction; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008139807,0.0004670927,0.0002774151,0.0003597716,0.000527307,0.001283136,0.0008653451,0.0009303188,0.005347436],"category_scores_gemma":[0.003346537,0.0001657319,0.0002986139,0.000177517,0.0005336404,0.00106719,0.001133002,0.0008740557,0.0006952381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002310548,"about_ca_system_score_gemma":0.0002722236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004053146,"about_ca_topic_score_gemma":0.0007183097,"domain_scores_codex":[0.9995264,0.0001964492,0.00002576873,0.00008151021,0.0001134341,0.0000565291],"domain_scores_gemma":[0.999145,0.0004576672,0.0001057048,0.0001100173,0.0001148709,0.0000667649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001324773,0.003526956,0.01068826,0.0005769454,0.0004520116,0.0009608536,0.005003592,0.08556798,0.06026423,0.06579068,0.02354838,0.7422953],"study_design_scores_gemma":[0.000432089,0.001660156,0.008319224,0.0001334968,0.0005459185,0.0005523289,0.00242747,0.8497368,0.02846547,0.05218318,0.05544572,0.00009811986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4266514,0.000555039,0.4476583,0.003595301,0.0008127349,0.0005579582,0.00009796561,0.004000871,0.1160703],"genre_scores_gemma":[0.9072387,0.0001086359,0.0807898,0.0005268747,0.00006925861,0.0001855418,0.00007237669,0.00008049072,0.01092826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005347436,"threshold_uncertainty_score":0.01788896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1204318733562563,"score_gpt":0.3595248876243088,"score_spread":0.2390930142680525,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}