{"id":"W4293443012","doi":"10.1111/imig.13017","title":"Being “top‐ranked” without “causing troubles”: Comparing federal and provincial immigration pathways for Chinese international students in Canada","year":2022,"lang":"en","type":"article","venue":"International Migration","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Immigration; Deskilling; Human capital; Immigration policy; Selection (genetic algorithm); Demographic economics; Political science; Economic growth; Economics; Work (physics); Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001492389,0.0003309639,0.0004075487,0.001725237,0.008366631,0.002851335,0.001640879,0.0004904201,0.003232909],"category_scores_gemma":[0.004198612,0.0001893262,0.0005462215,0.003773811,0.0017947,0.0007184494,0.002612162,0.001128641,0.0002151963],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04230274,"about_ca_system_score_gemma":0.09891605,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9916793,"about_ca_topic_score_gemma":0.997359,"domain_scores_codex":[0.9981142,0.0001614679,0.00006701807,0.000136795,0.0002974069,0.001223117],"domain_scores_gemma":[0.9955799,0.0002286371,0.0005606231,0.00009151038,0.001416295,0.002123088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002526997,0.0001366016,0.9513238,0.00008347755,0.00003284026,0.0001690473,0.02357884,0.0001540872,0.0002685286,0.001010141,0.002328484,0.02066147],"study_design_scores_gemma":[0.00001369463,0.0000632624,0.8899769,0.0001071648,0.00002813983,0.00003063663,0.1065516,0.0003909111,0.0001577792,0.0001238925,0.002524326,0.00003173279],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968275,0.0001101404,0.00005151271,0.0005150335,0.00001128463,0.00004773716,0.0003310994,0.000004342091,0.002101342],"genre_scores_gemma":[0.9984597,0.0001159832,0.0001197548,0.0001076266,0.000002195165,0.00002777385,0.0002690247,0.00000346604,0.0008945385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9576973,"threshold_uncertainty_score":0.3069293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01358635086577621,"score_gpt":0.2973976249438593,"score_spread":0.2838112740780831,"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."}}