{"id":"W2303754236","doi":"","title":"Towards automated content analysis of discussion transcripts: A cognitive presence case","year":2016,"lang":"en","type":"article","venue":"QUT ePrints (Queensland University of Technology)","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Overfitting; Random forest; Artificial intelligence; Cognition; Computer science; Coding (social sciences); Machine learning; Natural language processing; Context (archaeology); Set (abstract data type); Psychology; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001475992,0.0001073931,0.000337405,0.0007503348,0.0000782276,0.000005842126,0.0005776886,0.0001432663,0.00002728086],"category_scores_gemma":[0.0001287294,0.00006873807,0.0001593378,0.00125227,0.0003206877,0.0001508481,0.0002492655,0.0001137416,0.000007095227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002773933,"about_ca_system_score_gemma":0.00005590432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003416862,"about_ca_topic_score_gemma":0.0001439815,"domain_scores_codex":[0.9991132,0.00005207901,0.0001725809,0.0003305523,0.0001574596,0.000174108],"domain_scores_gemma":[0.9990462,0.00005627784,0.0001892008,0.0004120635,0.0002399464,0.00005630821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002940149,0.001167141,0.3888882,0.0001326086,0.004843215,0.004150564,0.004340489,0.0002749015,0.03061464,0.03764265,0.0003759139,0.5272757],"study_design_scores_gemma":[0.009711561,0.001405171,0.5272997,0.001761303,0.00441687,0.0004217375,0.0132874,0.3899524,0.03991044,0.006667245,0.00361835,0.00154785],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8114913,0.00001717678,0.1799199,0.007858849,0.00002842762,0.00007742213,0.00004657338,0.0003595283,0.0002008344],"genre_scores_gemma":[0.9931384,0.00004091535,0.006305819,0.000006829796,0.000002131758,1.488475e-7,0.000001969257,0.000002938519,0.000500884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5257279,"threshold_uncertainty_score":0.2803057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02202257027808128,"score_gpt":0.2504619449964253,"score_spread":0.2284393747183441,"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."}}