{"id":"W2057221192","doi":"10.2307/3552177","title":"Equalization, Efficiency and Migration: Watson Revisited","year":2003,"lang":"en","type":"article","venue":"Canadian Public Policy","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watson; Equalization (audio); Computer science; Algorithm; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.003245408,0.0007211081,0.000969297,0.002222731,0.001800742,0.004017093,0.00143462,0.00242734,0.008641865],"category_scores_gemma":[0.009585996,0.0003081988,0.001121134,0.003619953,0.009864166,0.006336207,0.002688342,0.004088045,0.0005213144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01288087,"about_ca_system_score_gemma":0.004707257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1086058,"about_ca_topic_score_gemma":0.04814257,"domain_scores_codex":[0.9966647,0.00113901,0.000128431,0.0004225318,0.001025089,0.0006201916],"domain_scores_gemma":[0.9979382,0.001119786,0.0001549749,0.0001628243,0.0005253363,0.00009883663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001390027,0.000005423123,0.0003947311,0.00003135078,0.00001265975,0.00003957837,0.0001461677,0.001506786,0.00003720321,0.982453,0.002102492,0.01325669],"study_design_scores_gemma":[0.00001740855,0.00001945488,0.001630212,0.00008900195,0.00002440829,0.00009469231,0.0003594872,0.003864847,0.0002260997,0.9533449,0.0403089,0.00002060072],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09980904,0.07021554,0.09203175,0.1429378,0.001943772,0.000107913,0.0003545584,0.0001492742,0.5924504],"genre_scores_gemma":[0.9282718,0.01737347,0.007947898,0.00590579,0.001325245,0.00005454644,0.00003712769,0.0001162962,0.0389679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1086058,"threshold_uncertainty_score":0.2159473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01975038824100211,"score_gpt":0.2918354273616923,"score_spread":0.2720850391206902,"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."}}