{"id":"W4415174060","doi":"10.62986/dp2013.30","title":"From Highly Skilled to Low Skilled: Revisiting the Deskilling of Migrant Labor","year":2013,"lang":"en","type":"report","venue":"","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Deskilling; Phenomenon; Immigration; Preference; Developed country; Brain drain; Work (physics)","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":[0.003663073,0.0005181924,0.0004958776,0.003430214,0.00624514,0.006792167,0.001890811,0.002515682,0.001874798],"category_scores_gemma":[0.005771873,0.0003223394,0.0004825502,0.003328078,0.01806087,0.009626689,0.00573667,0.004435822,0.0002944786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006819373,"about_ca_system_score_gemma":0.007576316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04316354,"about_ca_topic_score_gemma":0.06728583,"domain_scores_codex":[0.9977078,0.001133298,0.0001363762,0.0002639059,0.0003037245,0.0004547829],"domain_scores_gemma":[0.9950014,0.003015837,0.0007905362,0.0001537579,0.0007102315,0.0003282538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004135905,0.00007531111,0.02429685,0.001307591,0.00001760156,0.002634068,0.8014122,0.0001539863,0.0004932859,0.1095452,0.00484313,0.05517938],"study_design_scores_gemma":[0.000006563885,0.0001186172,0.04741159,0.003760132,0.00002533251,0.00200459,0.7877795,0.000223163,0.000330028,0.01994971,0.1383322,0.00005858317],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6495568,0.1775032,0.002253993,0.07457604,0.002149556,0.00008287729,0.0001336288,0.00002353398,0.0937203],"genre_scores_gemma":[0.9122239,0.07458953,0.000820882,0.006113684,0.0007570773,0.00005180048,0.00005919164,0.00003055992,0.005353326],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04316354,"threshold_uncertainty_score":0.08582461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01899557278718166,"score_gpt":0.3164587015870369,"score_spread":0.2974631287998553,"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."}}