{"id":"W2902473318","doi":"10.1080/1369183x.2018.1553675","title":"Fiscal burdens and knowledge of immigrant selection criteria","year":2018,"lang":"en","type":"article","venue":"Journal of Ethnic and Migration Studies","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Immigration; Dilemma; Immigration policy; Selection (genetic algorithm); Politics; Welfare state; Power (physics); Welfare; Political science; Demographic economics; Political economy; Sociology; Economics; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007107198,0.00005534205,0.0001710387,0.00008196658,0.0002205719,0.0000175701,0.00003253009,0.00004817729,0.00002599732],"category_scores_gemma":[0.0002803689,0.00004172344,0.00003340087,0.0001830116,0.0003743973,0.0001502631,0.00001435925,0.00006770516,8.182492e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002061348,"about_ca_system_score_gemma":0.00005796235,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001594629,"about_ca_topic_score_gemma":0.04068349,"domain_scores_codex":[0.9993507,0.0001238975,0.0002717797,0.00006119117,0.0001135413,0.00007892174],"domain_scores_gemma":[0.9989364,0.0001062292,0.0002125813,0.00002509024,0.0006716759,0.00004798733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001527006,0.0001100002,0.00699319,0.00007336307,0.0002477997,0.000001131502,0.9109271,0.000002324611,0.01417461,0.01241922,0.02665967,0.02823895],"study_design_scores_gemma":[0.00242157,0.002432064,0.1512942,0.0004843755,0.0004527981,0.00006997991,0.5686766,0.002281716,0.002843244,0.007235971,0.2612942,0.0005133215],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873989,0.009103011,0.0006007308,0.001952168,0.0002739433,0.00005311205,0.000002033994,0.000005611326,0.0006104435],"genre_scores_gemma":[0.983505,0.01445003,0.0003257119,0.00005398782,0.0003733766,7.94464e-7,2.646521e-7,0.000002498314,0.00128836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3422504,"threshold_uncertainty_score":0.9768215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06070080952318065,"score_gpt":0.4145100601754896,"score_spread":0.353809250652309,"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."}}