{"id":"W3102973193","doi":"10.3386/w28094","title":"Twisting the Demand Curve: Digitalization and the Older Workforce","year":2020,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Norges Forskningsråd; National Institute on Aging; Alfred P. Sloan Foundation; Social Sciences and Humanities Research Council of Canada; Ewing Marion Kauffman Foundation; National Science Foundation","keywords":"Earnings; Labour economics; Wage; Investment (military); Economics; Productivity; Workforce; Bargaining power; Demographic economics; Finance; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.008216783,0.000106543,0.0002051959,0.00006610792,0.0006524381,0.0003882079,0.0006087098,0.0001344182,0.0001493059],"category_scores_gemma":[0.002687013,0.00007020814,0.00009575519,0.0001272124,0.001854951,0.0001163757,0.000556228,0.0004218281,0.00001846119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004231304,"about_ca_system_score_gemma":0.000631595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003070268,"about_ca_topic_score_gemma":0.0007202167,"domain_scores_codex":[0.9972724,0.0008212663,0.0003900415,0.000356359,0.0009074527,0.000252491],"domain_scores_gemma":[0.9968987,0.002235918,0.0001835441,0.0002092768,0.0003969107,0.00007569634],"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":[0.00004129794,0.00002162426,0.01060405,0.00006343137,0.00005118215,1.090928e-7,0.006260315,0.0009068488,0.000001288903,0.975374,0.005611287,0.001064598],"study_design_scores_gemma":[0.0003797059,0.00001150167,0.003999478,0.00006704176,0.0000105927,1.827401e-7,0.002746936,0.002767317,0.000008301574,0.9856761,0.004238488,0.00009436193],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09526871,0.001436738,0.000175318,0.07491846,0.000582964,0.003126012,0.00004294346,0.00003748309,0.8244114],"genre_scores_gemma":[0.9975803,0.0006828636,0.00002317792,0.00009625939,0.0006141187,0.0001263433,0.00005626248,0.00001025648,0.0008104225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9023116,"threshold_uncertainty_score":0.6834645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.640289834386477,"score_gpt":0.5852715198302967,"score_spread":0.05501831455618034,"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."}}