{"id":"W2095857325","doi":"10.13025/23014","title":"Optimal Design of an Immigration Points System","year":2009,"lang":"en","type":"preprint","venue":"Arrow@dit (Dublin Institute of Technology)","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Earnings; Frontier; Human capital; Immigration; Selection (genetic algorithm); Econometrics; Economics; Quality (philosophy); Regression; Earnings growth; Regression analysis; Computer science; Statistics; Mathematics; Finance; Machine learning; Geography","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001403086,0.0003178146,0.0006696247,0.001001124,0.0002534953,0.00006592077,0.001346199,0.001528926,0.00003542297],"category_scores_gemma":[0.000413389,0.0003358375,0.0001619063,0.0009684472,0.0009606032,0.000475834,0.000203291,0.0006568097,0.00001487536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002736138,"about_ca_system_score_gemma":0.001023397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009456153,"about_ca_topic_score_gemma":0.003884149,"domain_scores_codex":[0.9973528,0.0001966073,0.000920423,0.0005746054,0.0005890555,0.0003665358],"domain_scores_gemma":[0.9972354,0.00002911138,0.001027946,0.0009078506,0.0006851243,0.0001145324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000092729,0.0004042896,0.000164642,0.0003195337,0.0001357368,0.00002873926,0.004221098,0.04784396,0.002269597,0.9284923,0.001322849,0.01470451],"study_design_scores_gemma":[0.006881662,0.003535101,0.001217475,0.008007776,0.001540227,0.0000880033,0.07952835,0.5085048,0.06316502,0.1135228,0.2072521,0.006756712],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8787185,0.0004051121,0.107923,0.004328327,0.002361155,0.001629025,0.0001277116,0.001024171,0.003482959],"genre_scores_gemma":[0.8935568,0.0003502363,0.1054771,0.00003826453,0.0001652908,0.00005598128,0.0001241238,0.0000212997,0.0002109224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8149695,"threshold_uncertainty_score":0.9999093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02262112379954501,"score_gpt":0.2904257189703185,"score_spread":0.2678045951707735,"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."}}