{"id":"W2280137029","doi":"","title":"La mobilità intellettuale cinese: un´analisi delle destinazioni e della composizione del capitale umano qualificato","year":2010,"lang":"it","type":"article","venue":"RIVISTA GEOGRAFICA ITALIANA","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Destinations; Human capital; Phenomenon; Political science; Brain drain; Geography; Economic geography; Economy; Economic growth; Economics; Tourism","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":[0.0003210054,0.0001962133,0.0001074263,0.001579184,0.0009296976,0.00262968,0.0002117535,0.0001618057,0.0105168],"category_scores_gemma":[0.0008321441,0.00006197504,0.00008424922,0.001899767,0.001022674,0.001288238,0.0008921858,0.0002465615,0.0004382743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001353915,"about_ca_system_score_gemma":0.001301272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02271443,"about_ca_topic_score_gemma":0.03276152,"domain_scores_codex":[0.9998713,0.0000192209,0.00000584863,0.00001912157,0.00003747104,0.00004715918],"domain_scores_gemma":[0.9996991,0.00005679,0.00006467172,0.00001830863,0.00006913705,0.0000920316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002073612,0.0001114614,0.3629883,0.0005035079,0.00005593797,0.001568471,0.2044716,0.0004590319,0.003631993,0.1084141,0.0168665,0.3007217],"study_design_scores_gemma":[0.000009069918,0.00006015315,0.7834423,0.0001891476,0.00002719288,0.0003111879,0.05940579,0.0003948378,0.0005838275,0.002715234,0.1528434,0.00001778614],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8616079,0.001681739,0.0003082639,0.001044698,0.0000331751,0.00002420884,0.0003143377,0.00001677438,0.1349688],"genre_scores_gemma":[0.9845473,0.0009271073,0.0001492615,0.00003178183,0.00002861198,0.00001077302,0.0001046173,0.000008346022,0.0141921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02271443,"threshold_uncertainty_score":0.04516447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01714360317334941,"score_gpt":0.278888615269041,"score_spread":0.2617450120956916,"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."}}