{"id":"W3123468508","doi":"10.1257/mac.20150222","title":"Land Reform and Productivity: A Quantitative Analysis with Micro Data","year":2020,"lang":"en","type":"article","venue":"American Economic Journal Macroeconomics","topic":"Land Rights and Reforms","field":"Agricultural and Biological Sciences","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; York University","funders":"","keywords":"Productivity; Agricultural economics; Land reform; Agriculture; Agricultural land; Context (archaeology); Land use; Transferability; Distribution (mathematics); Agricultural productivity; Economics; Business; Natural resource economics; Geography; Economic growth; Ecology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001992735,0.0001536801,0.0004138947,0.0000224858,0.0002240813,0.0001923997,0.0003630146,0.00002571088,0.0000929733],"category_scores_gemma":[0.00000573131,0.0000350622,0.00006465954,0.000147074,0.0002585129,0.0004308005,0.0001497849,0.0001764115,0.00002566314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008858954,"about_ca_system_score_gemma":0.00003313984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001573695,"about_ca_topic_score_gemma":0.005058913,"domain_scores_codex":[0.9990191,0.00002695053,0.0002635489,0.0004136699,0.00003482043,0.0002418864],"domain_scores_gemma":[0.9992374,0.0000456896,0.0003546146,0.0001020849,0.00001831354,0.0002418535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009724835,0.0001041623,0.5437074,0.00001433762,0.003600332,0.00005302179,0.001202593,0.0005932177,0.002595908,0.000491159,0.0006693067,0.4459961],"study_design_scores_gemma":[0.001592789,0.004351684,0.5034299,0.00002183084,0.001180484,0.0009085631,0.0099475,0.01903393,0.0005557348,0.0009809037,0.4563096,0.001687129],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827089,0.00007874857,0.00003369625,0.01651025,0.00004678814,0.00008415701,0.0002269816,0.00001696052,0.0002935218],"genre_scores_gemma":[0.9966887,0.0007050329,0.001225108,0.0007510795,0.0004490853,0.000001501833,0.00007965297,0.000002427782,0.00009740533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4556402,"threshold_uncertainty_score":0.2822993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02647379605466664,"score_gpt":0.2335963220661569,"score_spread":0.2071225260114902,"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."}}