{"id":"W2957755297","doi":"10.1177/0003122419856347","title":"Does Immigration Reduce the Support for Welfare Spending? A Cautionary Tale on Spatial Panel Data Analysis","year":2019,"lang":"en","type":"article","venue":"American Sociological Review","topic":"Migration, Refugees, and Integration","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ludwig-Maximilians-Universität München; York University","keywords":"German; Immigration; Conceptualization; Welfare; Context (archaeology); Unemployment; Panel data; Demographic economics; Welfare state; Economics; Panel analysis; Ethnic group; Diversity (politics); Political science; Development economics; Geography; Econometrics; Economic growth; Politics; Law","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001745826,0.0001498801,0.0004593544,0.00003845601,0.0005942683,0.00004727623,0.0006026261,0.00007913241,0.002336311],"category_scores_gemma":[0.001055531,0.00006885207,0.0002772026,0.000524512,0.0005466238,0.000166186,0.00005403516,0.0001599067,0.0001307673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001083659,"about_ca_system_score_gemma":0.0001063609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002544016,"about_ca_topic_score_gemma":0.003506298,"domain_scores_codex":[0.9978311,0.0006593789,0.0003781975,0.000500414,0.0003643046,0.000266592],"domain_scores_gemma":[0.9984182,0.000464415,0.0003717201,0.0005666969,0.0001048116,0.00007413528],"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":[0.0001901847,0.0005619951,0.07301646,0.0003919315,0.001066432,0.000003007845,0.01070254,0.00002162701,0.0002981729,0.3531095,0.0764222,0.4842159],"study_design_scores_gemma":[0.00008457131,0.0003452171,0.03305395,0.00007198573,0.0004553783,3.986096e-7,0.004702101,0.0002347853,0.000006305684,0.001038558,0.9597889,0.0002178691],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5869309,0.01833423,0.02383002,0.2873273,0.003174728,0.01832182,0.002355126,0.0007955944,0.05893026],"genre_scores_gemma":[0.9681075,0.02533549,0.0003519096,0.00245416,0.0003754731,0.0001354691,0.001735058,0.000007105666,0.001497791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8833667,"threshold_uncertainty_score":0.9985757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06623592497601985,"score_gpt":0.3899737775444524,"score_spread":0.3237378525684326,"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."}}