{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003026291,0.000472356,0.00147583,0.001669686,0.00131461,0.002961353,0.001221385,0.00280193,0.01941403],"category_scores_gemma":[0.01729277,0.0004358779,0.0008887669,0.00221716,0.002726994,0.003352959,0.001528109,0.001152749,0.001561581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002943079,"about_ca_system_score_gemma":0.001746039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009064982,"about_ca_topic_score_gemma":0.006513717,"domain_scores_codex":[0.9985015,0.0004928436,0.00007028102,0.0002042935,0.0001783566,0.0005528009],"domain_scores_gemma":[0.9879212,0.006615447,0.003410672,0.0007969173,0.0003350301,0.0009206088],"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":[0.00127729,0.0007186164,0.06150618,0.0003763041,0.0001243601,0.001040959,0.001155366,0.2379124,0.002595622,0.6252932,0.01098512,0.05701441],"study_design_scores_gemma":[0.0005682018,0.0006377951,0.0339165,0.0001721563,0.0001747929,0.0005483883,0.001587258,0.3494194,0.001000255,0.6047142,0.007121856,0.0001391537],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8981088,0.001168912,0.04702774,0.007803104,0.00008087337,0.0001988411,0.0009501305,0.0002493864,0.04441213],"genre_scores_gemma":[0.9915139,0.0002667716,0.001301801,0.0002235574,0.00002753878,0.00002874362,0.00006579249,0.000007716729,0.006564199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01941403,"threshold_uncertainty_score":0.06494641,"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."}}