{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01582791,0.0009017839,0.0008816431,0.001982925,0.01164629,0.01708991,0.001469208,0.004545453,0.01024114],"category_scores_gemma":[0.03462966,0.0007923627,0.0007205843,0.001427407,0.0210487,0.02183784,0.005285572,0.004530543,0.005188982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003599943,"about_ca_system_score_gemma":0.005235493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004101555,"about_ca_topic_score_gemma":0.004295138,"domain_scores_codex":[0.9745398,0.0187394,0.0006900277,0.00180232,0.003026889,0.001201541],"domain_scores_gemma":[0.9797235,0.01097502,0.001336603,0.001795663,0.003134676,0.003034555],"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":[0.000165835,0.0001066758,0.004969412,0.0009573225,0.0001098082,0.001052327,0.1525507,0.0004197883,0.002199257,0.6321796,0.06981089,0.1354783],"study_design_scores_gemma":[0.00002546911,0.00007613911,0.002467724,0.001274811,0.00008271225,0.00119181,0.1120336,0.001107327,0.001186984,0.3369116,0.5435303,0.0001115976],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03726676,0.0236448,0.0876724,0.3471354,0.00738893,0.000391877,0.0003169814,0.001345352,0.4948376],"genre_scores_gemma":[0.8897603,0.01127374,0.04325059,0.01713836,0.002632312,0.0003813109,0.0002792455,0.000600179,0.03468391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01708991,"threshold_uncertainty_score":0.08370697,"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."}}