{"id":"W2110944932","doi":"10.1016/j.jdeveco.2013.05.005","title":"Contractual structure in agriculture with endogenous matching","year":2013,"lang":"en","type":"article","venue":"Journal of Development Economics","topic":"Land Rights and Reforms","field":"Agricultural and Biological Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sharecropping; Moral hazard; Matching (statistics); Economics; Margin (machine learning); Microeconomics; Principal–agent problem; Agency (philosophy); Imperfect; Incentive; Endogeny; Econometrics; Agriculture; Mathematics; Computer science; Statistics; Finance; Biology","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":[0.00007725194,0.00009439685,0.0001822787,0.0000139496,0.0000745756,0.00007067298,0.0001405008,0.00006624567,0.0002098358],"category_scores_gemma":[0.00000170839,0.00001480144,0.00003227916,0.00004769519,0.0000128481,0.0002491788,0.00001508143,0.000148881,0.000007750611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006035205,"about_ca_system_score_gemma":0.00003585039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006363015,"about_ca_topic_score_gemma":0.001005791,"domain_scores_codex":[0.9993819,0.000008429239,0.0003171437,0.00007512935,0.0000594006,0.0001579831],"domain_scores_gemma":[0.9996089,0.00002610707,0.0002262874,0.0000144274,0.00005024556,0.00007403338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002402148,0.0005024678,0.1039323,0.00003244611,0.0004269925,0.0001755235,0.007385728,0.001890548,0.1860534,0.0006008472,0.001032938,0.6977266],"study_design_scores_gemma":[0.001105842,0.0003655998,0.8483754,0.00008627971,0.00001169187,0.001160003,0.001955586,0.000009953262,0.01328592,0.003118475,0.1300742,0.000451086],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988517,0.00003902958,0.000001706349,0.0006582867,0.0001053068,0.00008819043,0.000002583403,0.00000334674,0.0002498702],"genre_scores_gemma":[0.998513,0.00009384914,0.0008702075,0.0001309417,0.000176222,0.000001020533,0.000006703223,6.327716e-7,0.00020745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7444431,"threshold_uncertainty_score":0.2297555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009404961385805669,"score_gpt":0.1539207651807065,"score_spread":0.1445158037949009,"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."}}