{"id":"W4417195035","doi":"10.1007/s43681-025-00886-3","title":"The anatomy of AI policies: a systematic comparative analysis of AI policies across the globe","year":2025,"lang":"en","type":"article","venue":"AI and Ethics","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Commonwealth Scientific and Industrial Research Organisation","keywords":"Globe; Corporate governance; Standardization; Key (lock); Resource (disambiguation)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06249365,0.0004261104,0.001532221,0.0292959,0.001765363,0.005082657,0.001227419,0.001369725,0.002624196],"category_scores_gemma":[0.1479542,0.0005613819,0.001630302,0.03039645,0.002968289,0.007344967,0.003273471,0.001415411,0.0001866661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01079609,"about_ca_system_score_gemma":0.02662428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01643303,"about_ca_topic_score_gemma":0.03269443,"domain_scores_codex":[0.9500836,0.02798185,0.01002116,0.002752236,0.007859098,0.001302105],"domain_scores_gemma":[0.7813253,0.1535842,0.02997202,0.006087584,0.02768614,0.001344772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004993193,0.0002519827,0.184798,0.1620341,0.006482031,0.001496248,0.2173711,0.00167135,0.00175139,0.05454922,0.006327224,0.3627681],"study_design_scores_gemma":[0.00009464789,0.000499206,0.2843711,0.2629454,0.008054882,0.0006933029,0.3224775,0.001513756,0.001668743,0.01004654,0.1074424,0.0001925657],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.666904,0.2803227,0.01003266,0.005745711,0.0003218203,0.00470023,0.005704914,0.00007295888,0.02619491],"genre_scores_gemma":[0.9292157,0.05698963,0.007999105,0.001043048,0.00003805039,0.002843647,0.001297523,0.00004228102,0.0005310802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06249365,"threshold_uncertainty_score":0.330502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07159241435445805,"score_gpt":0.5032627409151175,"score_spread":0.4316703265606594,"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."}}