{"id":"W3121928906","doi":"","title":"The Returns to Computer Use Revisited, Again","year":2006,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada); HEC Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Human Resources and Skills Development Canada; HEC Montréal; Rheinische Friedrich-Wilhelms-Universität Bonn","keywords":"Wage; Econometrics; Differential (mechanical device); Selection (genetic algorithm); Economics; Panel data; Computer science; Labour economics; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001319933,0.0004258048,0.001067651,0.004088627,0.003121436,0.005912796,0.001680926,0.00225179,0.03120095],"category_scores_gemma":[0.01274255,0.0002828308,0.0008046857,0.01159037,0.002610484,0.002488793,0.00233676,0.004094646,0.001415696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03233407,"about_ca_system_score_gemma":0.04923007,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9819725,"about_ca_topic_score_gemma":0.9861952,"domain_scores_codex":[0.9979103,0.0001630356,0.00006751708,0.0001830892,0.000886059,0.0007899165],"domain_scores_gemma":[0.9895993,0.002003066,0.001246098,0.0003806464,0.003656133,0.003114729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004747477,0.000182007,0.2790282,0.0005330732,0.0004635189,0.001166028,0.005016117,0.003459593,0.0002114402,0.2685542,0.2710648,0.1698462],"study_design_scores_gemma":[0.00007461133,0.00008208404,0.6154817,0.001410159,0.0005074233,0.0004997294,0.01281902,0.003336774,0.0003264731,0.02816652,0.3371649,0.0001306395],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2339284,0.1190049,0.0009736692,0.3563763,0.001858089,0.00007025798,0.028287,0.0002093231,0.2592921],"genre_scores_gemma":[0.8786134,0.03233714,0.0003097725,0.007449252,0.0005955524,0.00002697724,0.002190962,0.0001037798,0.07837316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9819725,"threshold_uncertainty_score":0.2346012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03109588282028147,"score_gpt":0.2721910233259457,"score_spread":0.2410951405056643,"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."}}