{"id":"W4382766421","doi":"10.48550/arxiv.2306.17070","title":"Interdisciplinary Methods in Computational Creativity: How Human Variables Shape Human-Inspired AI Research","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Creativity in Education and Neuroscience","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alberta Machine Intelligence Institute; Canadian Institute for Advanced Research","keywords":"Creativity; Context (archaeology); Scholarship; Scrutiny; Realm; Computational creativity; Cognitive science; Human intelligence; Reflexivity; Epistemology; Computer science; Psychology; Sociology; Artificial intelligence; Social science; Social psychology; Political science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002674594,0.0003578821,0.0005124438,0.001632595,0.0007080142,0.0002001201,0.001257127,0.0004453818,0.0009527159],"category_scores_gemma":[0.0002227638,0.0004445584,0.0001892156,0.001918331,0.0008404494,0.0002375761,0.003045955,0.001925668,0.0002005318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004811235,"about_ca_system_score_gemma":0.0002799376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006834346,"about_ca_topic_score_gemma":0.0004402879,"domain_scores_codex":[0.994423,0.002609563,0.0003162707,0.001770824,0.0002289751,0.0006513496],"domain_scores_gemma":[0.9965903,0.001640907,0.0002358852,0.001043901,0.0002889084,0.0002001624],"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":[0.0002708605,0.003901571,0.1065638,0.0003590694,0.0003164986,0.002156438,0.0167615,0.07868095,0.003907706,0.7578375,0.02730953,0.001934592],"study_design_scores_gemma":[0.001803245,0.000428888,0.3192402,0.0005030946,0.0001049295,0.0000220389,0.01045972,0.08932839,0.0001562881,0.5752354,0.001445621,0.001272162],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9395205,0.00002296606,0.03553518,0.00268566,0.001688233,0.0008049911,0.00008975747,0.0003421624,0.01931054],"genre_scores_gemma":[0.9699066,0.00001063083,0.0007149957,0.0001020882,0.0001410256,0.00001833913,0.0001291576,0.00005183461,0.02892538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2126765,"threshold_uncertainty_score":0.9999605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.470425276268835,"score_gpt":0.4644030078266037,"score_spread":0.006022268442231304,"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."}}