{"id":"W2733223054","doi":"10.1017/s1365100516001012","title":"DIMINISHING RETURNS AND LABOR MARKET ADJUSTMENTS","year":2017,"lang":"en","type":"article","venue":"Macroeconomic Dynamics","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Economics; Rigidity (electromagnetism); Unemployment; Econometrics; Marginal product; Matching (statistics); Wage; Volatility (finance); Marginal product of labor; Productivity; Marginal utility; Labour economics; Microeconomics; Macroeconomics; Production (economics); Mathematics; Statistics","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":[0.001038974,0.0002760852,0.0005486766,0.0001177165,0.0005684415,0.0008182662,0.00071003,0.0001718542,0.000584608],"category_scores_gemma":[0.0002633533,0.0003368609,0.0001108009,0.00003641361,0.0002023617,0.0006691219,0.0004654946,0.0002412546,0.0001297476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002932999,"about_ca_system_score_gemma":0.00002437327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005316755,"about_ca_topic_score_gemma":0.0007281808,"domain_scores_codex":[0.9980593,0.00002094326,0.0007213755,0.0006939772,0.00003093119,0.0004734687],"domain_scores_gemma":[0.9977407,0.00007074049,0.0008199856,0.001160637,0.00002967474,0.0001782798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002145917,0.00003285726,0.5920666,0.00003426362,0.0000632079,0.000009779919,0.0001047812,0.000006055698,0.000001384003,0.404821,0.0002432254,0.00259541],"study_design_scores_gemma":[0.0009614955,0.00003631156,0.721403,0.00002105075,0.00001295115,0.000008552379,0.000102178,0.07779206,0.000002835241,0.1915068,0.007637209,0.0005155652],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9074453,0.00034847,0.0003177465,0.002221246,0.001086548,0.0002237872,0.001741193,0.00005294598,0.08656276],"genre_scores_gemma":[0.9918828,0.0004897336,0.0008624467,0.00052285,0.0001332087,0.00002304292,0.00005068959,0.00005471203,0.005980505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2133142,"threshold_uncertainty_score":0.9999083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01960211284258238,"score_gpt":0.2382248470877954,"score_spread":0.218622734245213,"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."}}