{"id":"W4288359812","doi":"10.1145/3290605.3300705","title":"What Makes a Good Conversation?","year":2019,"lang":"en","type":"preprint","venue":"","topic":"AI in Service Interactions","field":"Computer Science","cited_by":443,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Irish Research Council","keywords":"Conversation; Context (archaeology); Computer science; Key (lock); Human–computer interaction; Common ground; Term (time); Conversation analysis; Cognitive science; Psychology; Communication; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001221653,0.0001927886,0.0001952815,0.0001237441,0.00003943439,0.001377991,0.001767551,0.0001959044,0.0004702809],"category_scores_gemma":[0.00001291457,0.0001831784,0.0001242128,0.0001169081,0.00001700716,0.001562707,0.002337576,0.0004666949,0.003653379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110234,"about_ca_system_score_gemma":0.0001974106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001676071,"about_ca_topic_score_gemma":0.00006854246,"domain_scores_codex":[0.9986283,0.00005024777,0.0002434787,0.0006022637,0.0002860982,0.0001895665],"domain_scores_gemma":[0.9977862,0.0001631415,0.0001685825,0.001630787,0.0001911023,0.00006016314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001768607,0.000646047,0.004901048,0.000980595,0.0006872524,0.00007047389,0.03722293,0.02543229,0.0004179195,0.7541465,0.1200434,0.0554339],"study_design_scores_gemma":[0.0005769945,0.00009769691,0.003058681,0.0008394212,0.00005939238,0.00007035662,0.004212513,0.7624089,0.002731499,0.08950065,0.1348614,0.001582534],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002188921,0.0001823039,0.8948635,0.02965171,0.02179376,0.0005281746,0.000004244515,0.0006584589,0.05012897],"genre_scores_gemma":[0.8354309,0.0003282803,0.1148035,0.007996457,0.0004406192,0.0001349976,0.00004782558,0.00004020983,0.04077727],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8332419,"threshold_uncertainty_score":0.9996587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232976831261744,"score_gpt":0.2874159599699713,"score_spread":0.2641182768437969,"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."}}