{"id":"W4366384160","doi":"10.3138/cjpe.22.004","title":"Using Public Databases to Study Relative Program Impact","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Program Evaluation","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Impact assessment; Database; Computer science; Control (management); Impact evaluation; Statistics; Political science; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.1358281,0.0007629352,0.001612023,0.02228991,0.001712748,0.005167927,0.003163671,0.001618373,0.00429649],"category_scores_gemma":[0.4093979,0.0007389043,0.001359205,0.04539038,0.001398568,0.005904757,0.004137615,0.001686753,0.0005877045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006525414,"about_ca_system_score_gemma":0.01328475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03614013,"about_ca_topic_score_gemma":0.02403184,"domain_scores_codex":[0.7948192,0.1382796,0.02187849,0.006742017,0.03640224,0.001878536],"domain_scores_gemma":[0.3959359,0.4267444,0.06581035,0.05809425,0.05058134,0.002833815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003736918,0.00210207,0.6225036,0.006304589,0.006113406,0.0002812121,0.002963094,0.02164855,0.000493855,0.05721881,0.02973462,0.2468993],"study_design_scores_gemma":[0.004094995,0.002723168,0.699891,0.005973316,0.007341967,0.001009129,0.01153168,0.08274887,0.008757305,0.06302207,0.1123255,0.0005810625],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6071491,0.00721719,0.117332,0.007663191,0.0004741046,0.007451002,0.2081043,0.001196969,0.04341206],"genre_scores_gemma":[0.8375343,0.001577325,0.07873173,0.0004921082,0.0001767241,0.00934172,0.07126604,0.0001400028,0.0007400308],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1358281,"threshold_uncertainty_score":0.7183365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7796421792303019,"score_gpt":0.6674537745782021,"score_spread":0.1121884046520998,"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."}}