{"id":"W3175383928","doi":"10.1257/mac.20240026","title":"Search, Screening, and Sorting","year":2025,"lang":"en","type":"article","venue":"American Economic Journal Macroeconomics","topic":"Names, Identity, and Discrimination Research","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sorting; Computer science; Information retrieval; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001733957,0.0001048566,0.0002425207,0.000335954,0.001093912,0.0006996226,0.0003548438,0.00003974324,0.0003114561],"category_scores_gemma":[0.0002043481,0.0001232335,0.00009275155,0.0001045308,0.001129836,0.0004551882,0.0001393615,0.0003100748,0.00007666056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004374598,"about_ca_system_score_gemma":0.0006664445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005137956,"about_ca_topic_score_gemma":0.003436582,"domain_scores_codex":[0.9986243,0.0001909543,0.0003932659,0.0002340705,0.00008222638,0.0004752453],"domain_scores_gemma":[0.9990637,0.0002487137,0.0002298986,0.0001291218,0.00006405701,0.0002645445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003608555,0.00002676068,0.4648188,0.00001213487,0.000112923,0.000008002539,0.002083769,0.0002520392,0.0000156402,0.1091431,0.00755191,0.4159388],"study_design_scores_gemma":[0.003078471,0.000247898,0.1977035,0.0001037716,0.0001326046,0.00008722669,0.1294248,0.005796188,0.0002446943,0.09479459,0.5671711,0.001215189],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8895214,0.0002700816,0.001555217,0.008638762,0.0004206735,0.0001301828,0.000006466931,0.00003021953,0.09942698],"genre_scores_gemma":[0.9885311,0.003133034,0.001410382,0.0009128422,0.000372548,0.000004500072,0.000001336427,0.00001285783,0.005621471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5596192,"threshold_uncertainty_score":0.8413599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0216838551717397,"score_gpt":0.3644738766741744,"score_spread":0.3427900215024347,"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."}}