{"id":"W2268583882","doi":"10.1080/1070289x.2015.1091317","title":"Culturally tailored workers for specialised destinations: producing Filipino migrant subjects for export","year":2015,"lang":"en","type":"article","venue":"Identities","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"University of British Columbia Graduate School","keywords":"Destinations; Workforce; Scholarship; Migrant workers; State (computer science); Ideal (ethics); Political science; Business; Sociology; Demographic economics; Economic growth; Economics","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":[0.0006761542,0.0000896556,0.0001239779,0.00007346002,0.0004700124,0.0002848897,0.0001673814,0.00005739587,0.00003752972],"category_scores_gemma":[0.001909665,0.00008612558,0.00008344085,0.0001951524,0.0001538183,0.0004374566,0.00001214096,0.00003519241,0.00000856134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001343248,"about_ca_system_score_gemma":0.0003722524,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000979755,"about_ca_topic_score_gemma":0.0488836,"domain_scores_codex":[0.9990131,0.00004934798,0.0002119694,0.0001902484,0.0002881159,0.0002472703],"domain_scores_gemma":[0.9989166,0.0001199376,0.0001094319,0.0001081232,0.0006467766,0.00009918697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001298121,0.00009677796,0.01187823,0.0001091177,0.0000617702,0.00000246129,0.3571812,0.000327448,0.0003305208,0.09134916,0.5368084,0.00172515],"study_design_scores_gemma":[0.00237577,0.0001384528,0.01003729,0.0001383511,0.0001064176,0.000002254744,0.2985847,0.00145072,0.0004155102,0.04835835,0.6376921,0.0007000425],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9702646,0.0003543629,0.01083978,0.005840819,0.00279728,0.002712869,0.00008939229,0.000297078,0.006803836],"genre_scores_gemma":[0.8141283,0.00005782495,0.01142774,0.0002841421,0.002006611,0.0006627642,0.0002111324,0.00002927177,0.1711922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1643884,"threshold_uncertainty_score":0.9684718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05825627803125124,"score_gpt":0.3276263017723761,"score_spread":0.2693700237411249,"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."}}