{"id":"W1585042058","doi":"10.3386/w25780","title":"Land Reform and Productivity: A Quantitative Analysis with Micro Data","year":2019,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Land Rights and Reforms","field":"Agricultural and Biological Sciences","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Productivity; Agricultural economics; Natural resource economics; Data science; Economics; Computer science; Economic growth","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007041032,0.000449849,0.0007925252,0.001469287,0.0004270491,0.00162344,0.001223074,0.0009864245,0.005026577],"category_scores_gemma":[0.02230275,0.0003903561,0.0009983735,0.003492115,0.0013013,0.001407329,0.000985904,0.001230923,0.0006115947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001709944,"about_ca_system_score_gemma":0.0006809887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02048214,"about_ca_topic_score_gemma":0.01211551,"domain_scores_codex":[0.9952543,0.003368708,0.0001419685,0.0004351384,0.0004777169,0.0003222794],"domain_scores_gemma":[0.9273354,0.05526536,0.01095079,0.00360633,0.001730359,0.001111767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006160442,0.001145668,0.7539285,0.0002723184,0.001046001,0.0004429857,0.0005467004,0.2141452,0.001097187,0.008646969,0.003694959,0.01441745],"study_design_scores_gemma":[0.0003016771,0.001195342,0.5281348,0.00004019305,0.0003263057,0.0001938715,0.00113943,0.4536304,0.001652334,0.009460932,0.003823908,0.0001008226],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9746273,0.0002604086,0.01428612,0.0009814743,0.00001084946,0.00015769,0.007429755,0.000122609,0.002123952],"genre_scores_gemma":[0.9943355,0.00007382805,0.002647342,0.00006290265,0.00001815159,0.0001157421,0.002071929,0.00001394364,0.0006605329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02048214,"threshold_uncertainty_score":0.04072589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3160178309542457,"score_gpt":0.4467094687940369,"score_spread":0.1306916378397912,"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."}}