{"id":"W4408348802","doi":"10.1007/s00146-025-02284-z","title":"Old wine in new bottles: shifting to flexible regulatory approaches for generative AI","year":2025,"lang":"en","type":"article","venue":"AI & Society","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Wine; Generative grammar; Performing arts; Artificial intelligence; Computer science; Visual arts; Art","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":[0.001704906,0.0001313065,0.0002321339,0.00003592198,0.0007904696,0.0002291853,0.0002708431,0.0002947485,0.00002618049],"category_scores_gemma":[0.000649156,0.0001368534,0.0002137947,0.0005912178,0.0002109705,0.000350621,0.00006124496,0.0003327968,0.000005267293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003166982,"about_ca_system_score_gemma":0.001368505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004783271,"about_ca_topic_score_gemma":0.00489704,"domain_scores_codex":[0.99862,0.00009812095,0.000229401,0.0003076967,0.0002736881,0.0004711242],"domain_scores_gemma":[0.9991742,0.0002821535,0.00005385222,0.0001648662,0.0001534721,0.000171448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001183096,0.00006399033,0.00268439,0.00003690983,0.00006587132,2.955399e-7,0.2360165,0.0001686135,0.0002437758,0.4669663,0.2860961,0.007645491],"study_design_scores_gemma":[0.001649685,0.0001475747,0.01602543,0.0002329059,0.00006932897,8.22269e-8,0.1400772,0.001251223,0.002349342,0.3881508,0.4492453,0.0008011241],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1718487,0.001161556,0.02923258,0.7382216,0.001111115,0.002243427,0.00003177447,0.0003286598,0.05582055],"genre_scores_gemma":[0.8769746,0.0001241221,0.01444609,0.07011323,0.001548706,0.00006149682,0.00001131541,0.00002706948,0.03669334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7051259,"threshold_uncertainty_score":0.7230906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09199042697801106,"score_gpt":0.3941464148142352,"score_spread":0.3021559878362241,"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."}}