{"id":"W2071121251","doi":"10.1002/pam.20231","title":"The effects of state policy design features on take‐up and crowd‐out rates for the state children's health insurance program","year":2006,"lang":"en","type":"article","venue":"Journal of Policy Analysis and Management","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Robert Wood Johnson Foundation","keywords":"Crowding out; Outreach; Crowding; Actuarial science; Public economics; Asset (computer security); Quarter (Canadian coin); Business; Demographic economics; Private insurance; Medicaid; Economics; Health care; Economic growth; Psychology; Monetary economics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01069461,0.0005502725,0.0009856283,0.001048527,0.0007177055,0.001830781,0.001118977,0.001255089,0.004767429],"category_scores_gemma":[0.02889293,0.0004903472,0.002507751,0.0009062017,0.001145538,0.0008923222,0.001503262,0.002298535,0.0003058643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002853618,"about_ca_system_score_gemma":0.001783083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0184673,"about_ca_topic_score_gemma":0.02217635,"domain_scores_codex":[0.9916734,0.00450315,0.000434155,0.0006839267,0.001047239,0.001658004],"domain_scores_gemma":[0.9000688,0.06977622,0.02056903,0.003415391,0.002396228,0.003774198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006233171,0.002956787,0.8838705,0.0002664863,0.002202838,0.0002596591,0.0007201106,0.05840712,0.006492555,0.003106816,0.001302028,0.03418205],"study_design_scores_gemma":[0.0002013439,0.004507089,0.9680917,0.00004390546,0.0008390627,0.00006753265,0.0009335766,0.0198985,0.003658928,0.0006777127,0.001024061,0.00005659573],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974797,0.0001920522,0.0004034168,0.000176527,0.00001265898,0.00005123393,0.0004443937,0.00002748429,0.001212472],"genre_scores_gemma":[0.9987845,0.00005399081,0.000269325,0.00004334226,0.000009439193,0.00005653161,0.0002938033,0.000004239543,0.0004848406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0184673,"threshold_uncertainty_score":0.05655915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01882388277020206,"score_gpt":0.3139674658133166,"score_spread":0.2951435830431145,"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."}}