{"id":"W2110029939","doi":"10.3386/w10959","title":"Sorting It Out: International Trade and Protection With Heterogeneous Workers","year":2004,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sorting; Business; International trade; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003108911,0.0004734525,0.001058083,0.001336795,0.002634098,0.004333561,0.0008208816,0.002863324,0.01596518],"category_scores_gemma":[0.009935189,0.0002919031,0.0008463991,0.003977071,0.002222242,0.005786251,0.002233594,0.002320388,0.0008002643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001894811,"about_ca_system_score_gemma":0.004748572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03101697,"about_ca_topic_score_gemma":0.0672466,"domain_scores_codex":[0.9981897,0.0005223729,0.00007242245,0.0001588156,0.0002684756,0.0007881625],"domain_scores_gemma":[0.9921934,0.003093214,0.002161385,0.0009264607,0.0006572081,0.0009683623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001094326,0.0009105318,0.1635685,0.0003158492,0.0002078826,0.001656414,0.007874329,0.006521723,0.0004933627,0.5230481,0.1268931,0.1674159],"study_design_scores_gemma":[0.0003146256,0.0003071292,0.1593542,0.001204146,0.000354724,0.0005408578,0.03160441,0.005255994,0.001653977,0.7129037,0.0864141,0.00009226643],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6164608,0.01204489,0.01017995,0.07854921,0.0006690328,0.0002456252,0.004227537,0.00008892302,0.277534],"genre_scores_gemma":[0.9626311,0.003310131,0.001087265,0.002625571,0.0002756308,0.00005081957,0.0008638655,0.00002388043,0.02913171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03101697,"threshold_uncertainty_score":0.06167287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4881618911850027,"score_gpt":0.583005286821344,"score_spread":0.09484339563634125,"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."}}