{"id":"W3017512041","doi":"10.1080/13876988.2020.1762077","title":"Policy Learning in Comparative Policy Analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Comparative Policy Analysis Research and Practice","topic":"Policy Transfer and Learning","field":"Social Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 European Research Council; Concordia University","keywords":"Policy learning; Causality (physics); Causation; Policy analysis; Set (abstract data type); Relation (database); Probabilistic logic; Computer science; Epistemology; Political science; Positive economics; Management science; Artificial intelligence; Machine learning; Economics; Public administration; Data mining; Law","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":["metaresearch","metaepi_narrow","bibliometrics","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0091422,0.0002717621,0.001471975,0.008256705,0.00111872,0.000567525,0.0006905174,0.0001541186,0.0001488895],"category_scores_gemma":[0.0151663,0.000247204,0.0005478332,0.03437465,0.0009900157,0.001639675,0.0001251827,0.002542708,0.00003908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004980469,"about_ca_system_score_gemma":0.003439947,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06910327,"about_ca_topic_score_gemma":0.01092761,"domain_scores_codex":[0.9843199,0.01129721,0.001019412,0.000413013,0.001892931,0.00105748],"domain_scores_gemma":[0.9877462,0.008463289,0.0006564649,0.0001962512,0.001745721,0.001192037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003668372,0.0007212483,0.04519763,0.00003971487,0.01885332,0.0001679221,0.6966668,0.06446888,0.0007693184,0.1651099,0.001756367,0.002580536],"study_design_scores_gemma":[0.003611263,0.002983184,0.1416493,0.00008188857,0.005140249,0.00004255148,0.3124686,0.01930329,0.0003492716,0.0101691,0.5030547,0.001146575],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3090557,0.001714094,0.005960853,0.4291574,0.00002489891,0.0004314456,0.00002550949,0.00004659817,0.2535834],"genre_scores_gemma":[0.9933287,0.00227637,0.000465507,0.001372832,0.00176835,0.000006327262,0.000006327627,0.00001092025,0.0007646497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.684273,"threshold_uncertainty_score":0.999998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3382077729965742,"score_gpt":0.5881449315568932,"score_spread":0.249937158560319,"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."}}