{"id":"W1493129960","doi":"10.3386/w23128","title":"Land Misallocation and Productivity","year":2017,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Land Rights and Reforms","field":"Agricultural and Biological Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Ministerio de Economía y Competitividad","keywords":"Total factor productivity; Productivity; Agriculture; Panel data; Agricultural productivity; Capital (architecture); Inequality","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001086156,0.0001998461,0.0002946182,0.001278572,0.0004110644,0.001425494,0.0003144749,0.000253385,0.003780959],"category_scores_gemma":[0.006886298,0.0001821232,0.0002907443,0.003142655,0.001219295,0.001061084,0.001607647,0.0004712907,0.0004199004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001822459,"about_ca_system_score_gemma":0.0007163276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01484,"about_ca_topic_score_gemma":0.028389,"domain_scores_codex":[0.99874,0.0004417418,0.0001041876,0.0001525068,0.0002268895,0.0003346041],"domain_scores_gemma":[0.9924932,0.001531981,0.004322455,0.0007325995,0.0005536751,0.0003660604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004886879,0.00002371758,0.9824715,0.00003092869,0.00006177877,0.0001500794,0.0006336849,0.003093484,0.0006245477,0.003503582,0.0004300353,0.008927785],"study_design_scores_gemma":[0.0000029146,0.00002138877,0.9929233,0.00001468423,0.0000113586,0.00008877709,0.0005686162,0.001963766,0.0004985993,0.001827995,0.002074128,0.000004449637],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940646,0.0003420104,0.001119754,0.0004059751,0.00000379956,0.00001165802,0.0009191037,0.000009368508,0.003123771],"genre_scores_gemma":[0.9983429,0.00009916993,0.0002285866,0.00002345756,0.000005896936,0.000007563299,0.000281376,0.000002578486,0.001008393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01484,"threshold_uncertainty_score":0.02950728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.332625290160024,"score_gpt":0.464080553166523,"score_spread":0.131455263006499,"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."}}