{"id":"W2055416354","doi":"10.1126/science.1256911","title":"Legislators learning to interpret evidence for policy","year":2014,"lang":"en","type":"article","venue":"Science","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McMaster University","funders":"","keywords":"Policy learning; Political science; Computer science; Machine learning","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2070705,0.001054499,0.002007304,0.005248923,0.008465288,0.0159089,0.00389389,0.02261504,0.01533895],"category_scores_gemma":[0.400033,0.002397336,0.0026005,0.002128979,0.02155675,0.0194007,0.009637156,0.05930377,0.003864503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009128478,"about_ca_system_score_gemma":0.05392518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006767232,"about_ca_topic_score_gemma":0.01250026,"domain_scores_codex":[0.8764143,0.081759,0.006998974,0.007866562,0.01960632,0.007354832],"domain_scores_gemma":[0.5146675,0.4007002,0.01541905,0.02086137,0.0349213,0.01343069],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00009331343,0.000653431,0.008248406,0.001261576,0.0004276055,0.0003351703,0.01279136,0.002439409,0.001423617,0.4674266,0.3140334,0.1908661],"study_design_scores_gemma":[0.0001281044,0.00009809226,0.003145667,0.003869821,0.0001763919,0.0001428657,0.004793903,0.003799745,0.001447865,0.6294596,0.3528024,0.0001355219],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.003163275,0.00219453,0.03847859,0.9216182,0.003629455,0.0001645323,0.00008484838,0.000188964,0.03047767],"genre_scores_gemma":[0.2059022,0.007543536,0.1683865,0.584434,0.007239212,0.001251183,0.000313318,0.0006101835,0.02431983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7929295,"threshold_uncertainty_score":0.9778233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7619006591617496,"score_gpt":0.7685151394816164,"score_spread":0.006614480319866778,"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."}}