{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001571468,0.0001212055,0.0003464434,0.0001119938,0.0001211215,0.00009171056,0.0005353306,0.0001557827,0.00009754944],"category_scores_gemma":[0.00003762731,0.00002452938,0.0000559101,0.0002161987,0.0002206457,0.0001585678,0.0008127163,0.0003599502,0.00001542147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001569122,"about_ca_system_score_gemma":0.0001532877,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008106794,"about_ca_topic_score_gemma":0.01176588,"domain_scores_codex":[0.9985166,0.00007417791,0.0002150706,0.0006210259,0.0003787548,0.0001943771],"domain_scores_gemma":[0.9990172,0.0002652896,0.0001491255,0.0001862248,0.0003262439,0.00005595011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00402515,0.00197341,0.5894522,0.00152448,0.02070557,0.00002912289,0.001820755,0.007822334,0.01664868,0.2707958,0.009393373,0.07580918],"study_design_scores_gemma":[0.00130881,0.001775449,0.5172849,0.0003224531,0.0004822226,0.00002820327,0.001024383,0.0213355,0.001427561,0.4140116,0.03964157,0.001357344],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794413,0.0002928624,0.000001545578,0.005034602,0.0000721981,0.0005154429,0.000974916,0.000009225006,0.0136579],"genre_scores_gemma":[0.9935713,0.0003576446,0.0002767558,0.000008172357,0.0002291213,0.00001334861,0.002740017,0.000001270011,0.002802404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1432158,"threshold_uncertainty_score":0.9984983,"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."}}