{"id":"W2547096835","doi":"10.3233/aac-160009","title":"Towards a richer model of deliberation dialogue: Closure problem and change of circumstances","year":2016,"lang":"en","type":"article","venue":"Argument & Computation","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Social Sciences and Humanities Research Council of Canada; Engineering and Physical Sciences Research Council; University of Aberdeen; Research Councils UK","keywords":"Deliberation; Computer science; Closure (psychology); Natural (archaeology); Set (abstract data type); Extension (predicate logic); Epistemology; Management science; Artificial intelligence; Political science; Engineering; Geography; Philosophy","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":[],"consensus_categories":[],"category_scores_codex":[0.0002673456,0.00008721524,0.0001463866,0.00008503202,0.00003568223,0.00002163697,0.0001061222,0.00004280871,0.000001804881],"category_scores_gemma":[0.000009212208,0.00006372839,0.00003101203,0.0001310705,0.00002200157,0.0006072664,0.00004767433,0.00001998532,0.000002185731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000305907,"about_ca_system_score_gemma":0.00002965823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006457759,"about_ca_topic_score_gemma":0.00001763757,"domain_scores_codex":[0.9990491,0.0000625883,0.0003204716,0.0002123212,0.0002548555,0.0001006353],"domain_scores_gemma":[0.9993703,0.00003985823,0.0002899345,0.0001134974,0.0001548611,0.00003154844],"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":[0.00003775452,0.0003641253,0.01921847,0.0004695868,0.00009659225,0.000001078093,0.0191967,0.008316264,0.1056978,0.07771672,0.0004140371,0.7684709],"study_design_scores_gemma":[0.001266385,0.0002212894,0.04197698,0.0002226078,0.00001858406,0.000001570125,0.00002526967,0.9192339,0.01100046,0.025783,0.00004503883,0.0002049047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2831153,0.0001209587,0.7158549,0.0004087655,0.00008277749,0.0002796324,0.000004584173,0.00002512831,0.0001080213],"genre_scores_gemma":[0.9754142,0.00005864745,0.02438813,0.00004099283,0.00003168873,0.00003116187,0.000005565497,0.000004882053,0.00002475941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9109176,"threshold_uncertainty_score":0.2598768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04854872818810164,"score_gpt":0.2641609590991205,"score_spread":0.2156122309110188,"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."}}