{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005053351,0.00007247052,0.00007646456,0.0003746375,0.0007000511,0.0002800658,0.0001071225,0.000091387,0.0005897699],"category_scores_gemma":[0.001843081,0.00007700623,0.00001909894,0.001259599,0.0001368442,0.0002353737,0.000004038888,0.00005590708,0.00003160999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003135488,"about_ca_system_score_gemma":0.004199116,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2592407,"about_ca_topic_score_gemma":0.9301151,"domain_scores_codex":[0.9989645,0.0001986317,0.000137444,0.0001539939,0.0001563207,0.0003890494],"domain_scores_gemma":[0.9988789,0.00002906052,0.00004152913,0.0001329407,0.0001722295,0.0007453228],"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":[1.167659e-7,0.000003891984,0.002320017,0.000002240413,0.000002050926,8.887e-7,0.004817677,0.000001683784,0.000003407725,0.9836152,0.007787758,0.00144509],"study_design_scores_gemma":[0.00008146281,0.000005606066,0.0007109296,0.000003150338,0.000002194353,0.000001886609,0.001021374,0.0003007063,0.00000186347,0.002288446,0.9954801,0.0001022699],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1739528,0.0009521802,0.002975393,0.1564688,0.0003413239,0.0006508071,0.0001031474,0.0002095373,0.6643459],"genre_scores_gemma":[0.9857278,0.0002547857,0.00007790643,0.004400325,0.000171287,0.000006817927,0.0000324208,0.00000806528,0.009320564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9876924,"threshold_uncertainty_score":0.7456921,"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."}}