{"id":"W4400927137","doi":"10.1007/s44217-024-00209-4","title":"Enhancing high-school dropout identification: a collaborative approach integrating human and machine insights","year":2024,"lang":"en","type":"article","venue":"Discover Education","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Dropout (neural networks); Machine learning; Random forest; Artificial intelligence; Socioeconomic status; Identification (biology); Computer science; Human–machine system; Mathematics education; Data science; Psychology; Medicine","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":[0.0001753237,0.0001179305,0.0001055001,0.0001589943,0.0002056439,0.001031273,0.0002067957,0.00003926694,0.000006959398],"category_scores_gemma":[0.00006712491,0.00009894255,0.00002552401,0.0006324682,0.00003180345,0.0008588999,0.00006759484,0.000199378,0.00003146234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007602194,"about_ca_system_score_gemma":0.0004434726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001212653,"about_ca_topic_score_gemma":0.00003732076,"domain_scores_codex":[0.999038,0.00007072892,0.0002221242,0.0003955321,0.0001637145,0.0001099309],"domain_scores_gemma":[0.999453,0.00004133167,0.00007374425,0.0002659228,0.0000969881,0.00006900074],"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":[0.000001460154,0.0002126113,0.001606092,0.00017082,0.00004642613,0.000002026019,0.01366282,0.00009067007,0.0118637,0.9553823,0.001386842,0.01557418],"study_design_scores_gemma":[0.0006817433,0.0003067192,0.03466889,0.001396687,0.0002110521,0.00006430591,0.02023994,0.6927276,0.02260735,0.2065281,0.01878211,0.001785523],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4067598,0.003594709,0.5783705,0.005682005,0.001298429,0.0002972283,0.000007601986,0.0003129141,0.003676896],"genre_scores_gemma":[0.981278,0.00001819385,0.01493169,0.00009477368,0.0002871797,0.0000264612,0.0000909261,0.00001065897,0.003262103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7488542,"threshold_uncertainty_score":0.9944589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00761925123134833,"score_gpt":0.2891912468949003,"score_spread":0.281571995663552,"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."}}