{"id":"W1580841871","doi":"","title":"Which Workers Gain from Computer Use","year":2012,"lang":"en","type":"book","venue":"RePEc: Research Papers in Economics","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Productivity; Wage; Labour economics; Control (management); Selection (genetic algorithm); Computer users; Economics; Business; Demographic economics; Computer science; Management; Economic growth; Computer security; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"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":[0.0003799626,0.0001576261,0.0002656494,0.0009373655,0.000515528,0.001514584,0.0002973886,0.0006275552,0.01812801],"category_scores_gemma":[0.002817668,0.0001483999,0.0003824997,0.0008684339,0.0005629486,0.001582696,0.0007005372,0.0005306799,0.004089592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005763563,"about_ca_system_score_gemma":0.0003957666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009407007,"about_ca_topic_score_gemma":0.02167426,"domain_scores_codex":[0.9995653,0.00005202691,0.00001532166,0.0000650677,0.00009524991,0.000206996],"domain_scores_gemma":[0.9990135,0.0002253835,0.0002386132,0.00007157611,0.0001164906,0.0003343132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004838779,0.0003312974,0.7119654,0.0001925478,0.0001771793,0.0003240375,0.001750321,0.0003051666,0.001710748,0.008335081,0.01601838,0.258406],"study_design_scores_gemma":[0.00003740139,0.0001641146,0.976324,0.0001089968,0.00006751327,0.0003980281,0.003013613,0.0002715256,0.0004010003,0.006396364,0.01280039,0.00001704189],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.948697,0.006379116,0.0003786076,0.005583437,0.0001321884,0.00003617209,0.002333498,0.00004728415,0.03641263],"genre_scores_gemma":[0.9825546,0.002006759,0.00009929274,0.0006091602,0.0001006482,0.00001239386,0.0006093539,0.00001051386,0.01399734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01812801,"threshold_uncertainty_score":0.06064421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04997231742051728,"score_gpt":0.2735265247382219,"score_spread":0.2235542073177046,"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."}}