{"id":"W2352252490","doi":"10.1016/j.evalprogplan.2016.05.002","title":"Steps towards incorporating heterogeneities into program theory: A case study of a data-driven approach","year":2016,"lang":"en","type":"article","venue":"Evaluation and Program Planning","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Michael's Hospital","funders":"","keywords":"Program evaluation; Management science; Computer science; Engineering; Data science; Political science; Public administration","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":[0.007219104,0.000147895,0.0002537986,0.0002231751,0.0002877895,0.0002628874,0.0004506761,0.0000668029,0.00004359183],"category_scores_gemma":[0.0008119295,0.00009018421,0.00003581962,0.0005773054,0.0001349574,0.0005360654,0.000311291,0.00007605873,0.000003573567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002320751,"about_ca_system_score_gemma":0.00008939591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008122951,"about_ca_topic_score_gemma":0.00002880733,"domain_scores_codex":[0.9967243,0.000690117,0.0007118909,0.0005949887,0.001124646,0.0001540537],"domain_scores_gemma":[0.9974648,0.0006085153,0.0004644948,0.0008112935,0.0005649719,0.00008586734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002154335,0.0004143732,0.015071,0.000006206354,0.00002675003,0.000006240313,0.004379578,0.000256896,0.0000366254,0.0005478282,0.00007824548,0.9791547],"study_design_scores_gemma":[0.003517918,0.002739745,0.009828938,0.0001451338,0.000241507,0.0003194214,0.09029282,0.8437152,0.0002257053,0.0436767,0.004689402,0.0006075011],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9593332,0.0001556596,0.03628007,0.00009828604,0.00002853411,0.003101011,0.00001924692,0.0002258476,0.000758183],"genre_scores_gemma":[0.9303894,0.000001634449,0.06852753,0.00001808135,0.00004148986,0.0009313295,0.00003733407,0.00001143397,0.0000417376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9785472,"threshold_uncertainty_score":0.3677606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4316788458018273,"score_gpt":0.5556526633586825,"score_spread":0.1239738175568552,"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."}}