{"id":"W2180823206","doi":"10.1177/2158244015604193","title":"The Mobilization of Scientific Evidence by Public Policy Analysts","year":2015,"lang":"en","type":"article","venue":"SAGE Open","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Mobilization; Mediation; Public policy; Field (mathematics); Path analysis (statistics); Government (linguistics); Policy analysis; Political science; Public economics; Test (biology); Positive economics; Psychology; Econometrics; Economics; Public administration; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01760796,0.00005728779,0.0001192658,0.0001658715,0.0003312651,0.002167593,0.001908337,0.00002374389,0.0004525231],"category_scores_gemma":[0.006960357,0.00003157788,0.00003346959,0.002231861,0.0001844286,0.001618074,0.0004149674,0.00004454521,0.0003199457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006698335,"about_ca_system_score_gemma":0.001328924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001883786,"about_ca_topic_score_gemma":0.0006037095,"domain_scores_codex":[0.996955,0.0003826288,0.0004456075,0.0002568164,0.001805832,0.0001541215],"domain_scores_gemma":[0.9973562,0.0005401706,0.0002838069,0.0007678797,0.0009345441,0.0001173497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000601664,0.0000897705,0.1094353,0.000004119237,0.00002233831,4.411332e-7,0.002244226,0.0003506967,0.003248627,0.01224953,0.3100285,0.5622663],"study_design_scores_gemma":[0.0009755593,0.0002873795,0.03235815,0.00008177134,0.00001769883,0.000002626351,0.005935486,0.03384332,0.00534925,0.04692272,0.8739522,0.0002738577],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8319848,0.002964097,0.008260618,0.04682941,0.001509317,0.0019434,0.00003740659,0.00003606845,0.1064349],"genre_scores_gemma":[0.9765534,0.0000457612,0.0001472108,0.0001730759,0.00002632064,0.00002179111,0.000006496963,0.000003106001,0.02302281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5639237,"threshold_uncertainty_score":0.9988682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5517309626375979,"score_gpt":0.584428211461108,"score_spread":0.03269724882351011,"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."}}