{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004927967,0.0006415051,0.0005900548,0.0005477222,0.001344084,0.00377388,0.001220328,0.001608611,0.01532879],"category_scores_gemma":[0.02537949,0.0004369296,0.0006682268,0.0008792196,0.003177281,0.006656753,0.002918978,0.004588738,0.001077904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001532273,"about_ca_system_score_gemma":0.001579899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003374851,"about_ca_topic_score_gemma":0.003114236,"domain_scores_codex":[0.9978364,0.001450752,0.00004560744,0.0001911658,0.0003566039,0.0001195643],"domain_scores_gemma":[0.9839492,0.01401401,0.0002355619,0.001113936,0.0003808919,0.0003063982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0007911959,0.0005514146,0.001509942,0.0002720047,0.00008836784,0.0001425939,0.003975237,0.070492,0.004411321,0.8482335,0.002064279,0.06746823],"study_design_scores_gemma":[0.0002314158,0.0002079921,0.0004218905,0.00006223947,0.00005060293,0.00004998545,0.000891045,0.208443,0.004957776,0.7755437,0.009094428,0.00004586035],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.462311,0.0006856667,0.3757345,0.007239288,0.0005337768,0.0002548636,0.0001597216,0.001116279,0.151965],"genre_scores_gemma":[0.9293143,0.0001900051,0.06315429,0.0004802172,0.00002989534,0.0001008781,0.00006876449,0.0001292903,0.006532369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01532879,"threshold_uncertainty_score":0.0512799,"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."}}