{"id":"W2911465809","doi":"10.1109/lra.2019.2897143","title":"It Would Make Me Happy if You Used My Guess: Comparing Robot Persuasive Strategies in Social Human–Robot Interaction","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Compliance (psychology); Exploratory research; Warrant; Human–robot interaction; Psychology; Persuasive technology; Social psychology; Human–computer interaction; Social robot; Computer science; Applied psychology; Cognitive psychology; Artificial intelligence; Persuasion; Mobile robot; Robot control; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002094371,0.0002791649,0.0004222676,0.0003227068,0.0002650924,0.000325691,0.0001648199,0.0003023419,0.0006484353],"category_scores_gemma":[0.00001337829,0.0003112251,0.0001284177,0.0002259343,0.00009086497,0.0004737249,0.00003367605,0.0006953807,0.0003523732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001921792,"about_ca_system_score_gemma":0.00002612661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002628679,"about_ca_topic_score_gemma":0.0001838185,"domain_scores_codex":[0.9981671,0.0001713311,0.0005581272,0.0004445805,0.0002688274,0.0003900183],"domain_scores_gemma":[0.9991975,0.0001320911,0.0003357275,0.0001975844,0.00006813229,0.00006893851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004263172,0.001085632,0.0578834,0.0003274114,0.0008849045,0.00009873461,0.1644446,0.4391974,0.2122079,0.08384371,0.03454788,0.005052169],"study_design_scores_gemma":[0.0117375,0.0005558527,0.6882217,0.0008636301,0.0003312918,0.000112573,0.1599042,0.1211531,0.001309772,0.001528015,0.01109896,0.003183468],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9505146,0.00002592035,0.01565289,0.01564118,0.004694669,0.0006033882,0.000006057379,0.0002681855,0.01259316],"genre_scores_gemma":[0.9956184,0.000004938016,0.0003789186,0.0027878,0.0004457191,0.00003326488,0.00004522182,0.00004051609,0.0006452299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6303383,"threshold_uncertainty_score":0.999934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05851957109241265,"score_gpt":0.3643572810340588,"score_spread":0.3058377099416462,"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."}}