{"id":"W4320712692","doi":"10.32920/22096157","title":"The Analysis of Nonverbal Communication: The Dangers of Pseudoscience in Security and Justice Contexts","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Deception detection and forensic psychology","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; York University; Université de Montréal; Université Laval; Trinity College; McGill University; University of Toronto; Université du Québec à Trois-Rivières; Ministère de l’Emploi et de la Solidarité Sociale (Québec)","funders":"","keywords":"Pseudoscience; Nonverbal communication; Deception; Lie detection; Economic Justice; Scope (computer science); Psychology; Social psychology; Public relations; Work (physics); State (computer science); Engineering ethics; Political science; Law; Communication; Computer science; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.03260319,0.0005709928,0.0005567675,0.007084605,0.005289828,0.009161611,0.001541643,0.002856108,0.003164056],"category_scores_gemma":[0.08964229,0.0005068596,0.0004316188,0.003167099,0.03187301,0.01311257,0.006563205,0.003826246,0.0003453736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002668572,"about_ca_system_score_gemma":0.003921365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002404729,"about_ca_topic_score_gemma":0.00285369,"domain_scores_codex":[0.9432679,0.04570596,0.001362434,0.00193115,0.007008086,0.0007244133],"domain_scores_gemma":[0.7977187,0.1692516,0.01762781,0.009276419,0.005013436,0.001111979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001833208,0.0001235602,0.0196354,0.001298886,0.00006059078,0.001551114,0.369357,0.000492793,0.003863893,0.4213629,0.004095253,0.1779752],"study_design_scores_gemma":[0.00003242363,0.0002820873,0.05511673,0.003500023,0.00005399634,0.003142459,0.3741576,0.003922663,0.004629875,0.4592783,0.09570734,0.0001766117],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5693702,0.02750551,0.169919,0.0755944,0.001648628,0.0005377202,0.0002199571,0.000218203,0.1549865],"genre_scores_gemma":[0.9681153,0.004628836,0.02004355,0.00326069,0.0005727991,0.0003547177,0.00004199663,0.00007054871,0.002911471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9947101,"threshold_uncertainty_score":0.1724242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04974957966084741,"score_gpt":0.3842002249434112,"score_spread":0.3344506452825637,"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."}}