{"id":"W4392681872","doi":"10.22318/icls2023.547433","title":"Using Hierarchical Time Series Clustering to Capture the Trajectories of Epistemic Emotions: The Case of Confusion","year":2023,"lang":"en","type":"article","venue":"Proceedings.","topic":"Cognitive Science and Education Research","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University; University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Confusion; Hierarchical clustering; Cluster analysis; Cluster (spacecraft); Series (stratigraphy); Computer science; Dendrogram; Epistemology; Psychology; Data science; Cognitive psychology; Artificial intelligence; Sociology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003845949,0.0006348962,0.0004861083,0.002042328,0.0009283529,0.002037398,0.0005494399,0.0008055776,0.0008166548],"category_scores_gemma":[0.03459096,0.000187577,0.0005428112,0.001751308,0.001012551,0.001774597,0.001182823,0.001187178,0.0002414355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001010283,"about_ca_system_score_gemma":0.000600899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007263616,"about_ca_topic_score_gemma":0.006210885,"domain_scores_codex":[0.9984876,0.0006702351,0.00009690712,0.0002896712,0.0002999554,0.0001556319],"domain_scores_gemma":[0.9841481,0.01004208,0.002467972,0.001271375,0.001579558,0.0004908357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00207284,0.0004558806,0.4491175,0.000499749,0.0006712337,0.004305298,0.06329811,0.1270613,0.07225085,0.03658779,0.004104843,0.2395745],"study_design_scores_gemma":[0.00003819135,0.0003067841,0.2609006,0.0001191398,0.000132566,0.0009547091,0.01535117,0.6602897,0.01208796,0.04446471,0.005062621,0.0002918186],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8933351,0.0001779873,0.103246,0.000435015,0.0000412543,0.0001005704,0.0001890999,0.0001325365,0.002342579],"genre_scores_gemma":[0.9831116,0.00004607702,0.0163628,0.0000210657,0.00001294884,0.00003520064,0.0001007873,0.00002233028,0.0002872244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007263616,"threshold_uncertainty_score":0.02033955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1057326339244095,"score_gpt":0.3699624822650958,"score_spread":0.2642298483406863,"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."}}