{"id":"W3125369431","doi":"","title":"ICT-specific technological change and productivity growth in the US 1980-2004","year":2008,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Information and Communications Technology; Productivity; Technological change; Production (economics); Economics; Capital (architecture); Technical change; Industrial organization; Function (biology); Total factor productivity; Quarter (Canadian coin); Business; Economic growth; Macroeconomics; Computer science; Geography","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":[0.0005408556,0.0005523133,0.0003506309,0.002523764,0.0002815867,0.001216642,0.0003427715,0.0006659495,0.001749292],"category_scores_gemma":[0.003749962,0.0001830631,0.0005800563,0.005865644,0.0004394836,0.0009685478,0.0006244634,0.0007861928,0.0006260772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003795863,"about_ca_system_score_gemma":0.001521807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1374196,"about_ca_topic_score_gemma":0.103379,"domain_scores_codex":[0.9996865,0.00004074319,0.00003017132,0.00006807263,0.00008183991,0.00009268089],"domain_scores_gemma":[0.9986281,0.0003097592,0.0005368748,0.00006282844,0.0003285611,0.0001338018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006322413,0.0003969897,0.8304179,0.0003329111,0.0004408576,0.0009718094,0.001202713,0.05207305,0.001113983,0.0118523,0.02906544,0.07149981],"study_design_scores_gemma":[0.00004787488,0.0001337988,0.941148,0.0001149308,0.0002065596,0.0002783611,0.0009403225,0.01103174,0.001517057,0.003908187,0.0406333,0.00003981173],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668959,0.004422294,0.001046807,0.002225984,0.00009860544,0.00001930187,0.01618024,0.0001278768,0.008983027],"genre_scores_gemma":[0.9788641,0.003224148,0.0002906311,0.0001752441,0.0000629717,0.00001779764,0.01260391,0.00001643063,0.004744674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1374196,"threshold_uncertainty_score":0.2732395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09667569412234488,"score_gpt":0.2722394450857183,"score_spread":0.1755637509633735,"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."}}