{"id":"W4413482498","doi":"10.64628/aam.4hqxaj6h4","title":"Experimenting with generative AI to kibbitz and futz towards more inclusive futures","year":2025,"lang":"en","type":"article","venue":"","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Futures contract; Generative grammar; Computer science; Business; Artificial intelligence; Economics; Financial economics","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":[0.0009284599,0.0001898116,0.0003767619,0.0004567557,0.0005475789,0.0008012643,0.0004444347,0.00005345896,0.0004422411],"category_scores_gemma":[0.000628804,0.0001076997,0.00005836228,0.001078548,0.00006602272,0.0002371723,0.000909261,0.0001000127,0.00004099901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004171855,"about_ca_system_score_gemma":0.0001276231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002233917,"about_ca_topic_score_gemma":0.0004187325,"domain_scores_codex":[0.9973198,0.0001107746,0.0005371748,0.0006874387,0.001093199,0.0002516278],"domain_scores_gemma":[0.9982456,0.0004549517,0.0001102568,0.0005236403,0.0005210161,0.0001445514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003693299,0.0001025303,0.01463401,0.00001452309,0.0001375418,0.00009022166,0.03220133,0.00138081,0.01143548,0.2598741,0.3649807,0.3147794],"study_design_scores_gemma":[0.002820361,0.0005962893,0.0945411,0.0003757814,0.00004191158,0.00008290325,0.1747198,0.02195643,0.03025108,0.09622097,0.5769979,0.001395555],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5864934,0.001538121,0.3039477,0.01006283,0.0006873346,0.0006540534,0.000008840164,0.00009588544,0.09651184],"genre_scores_gemma":[0.9617769,8.373524e-7,0.01503926,0.006752904,0.0001454564,0.00003034378,3.546598e-7,0.000007956107,0.01624595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3752836,"threshold_uncertainty_score":0.772661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06132767970049612,"score_gpt":0.4431108865004493,"score_spread":0.3817832067999532,"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."}}