{"id":"W2125889474","doi":"","title":"The Measurement of Nonmarket Sector Outputs and Inputs Using Cost Weights","year":2008,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Economic, financial, and policy analysis","field":"Economics, Econometrics and Finance","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nonmarket forces; Subsidy; Economics; Productivity; Order (exchange); Production (economics); Returns to scale; Government (linguistics); Construct (python library); Scale (ratio); Total factor productivity; Microeconomics; Factor market; Macroeconomics; Computer science; Finance; Market economy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004957025,0.0004588067,0.00135965,0.001014592,0.0004938931,0.0001978842,0.0009346197,0.000565984,0.00006246738],"category_scores_gemma":[0.000630238,0.000485465,0.0004066613,0.00022334,0.0007054046,0.0001553498,0.0009815354,0.001278259,0.00002633011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001545318,"about_ca_system_score_gemma":0.0005322052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001254168,"about_ca_topic_score_gemma":0.001799957,"domain_scores_codex":[0.9955342,0.0001703692,0.001965072,0.001202012,0.0001404998,0.0009878563],"domain_scores_gemma":[0.9965841,0.000527721,0.001089007,0.001392519,0.0001756578,0.0002310653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001831927,0.001741028,0.5650029,0.002701768,0.005562225,0.0001244133,0.01125948,0.03583871,0.0003052566,0.1463182,0.002098337,0.2272158],"study_design_scores_gemma":[0.004591128,0.0003777749,0.1200452,0.001074947,0.0001081325,0.00005655835,0.0006365622,0.1540675,0.001419878,0.09341317,0.6203349,0.003874313],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8843521,0.004185189,0.00004519626,0.0005377218,0.000888607,0.001222804,0.0006051485,0.00002256331,0.1081407],"genre_scores_gemma":[0.9178028,0.08034782,0.0003885756,0.00009253591,0.0004066949,0.0001425996,0.000024341,0.00008662554,0.0007080609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6182365,"threshold_uncertainty_score":0.9997597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09856810122622908,"score_gpt":0.2956301984664226,"score_spread":0.1970620972401935,"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."}}