{"id":"W2279350356","doi":"10.1111/iere.12357","title":"TECHNOLOGY, POLICY DISTORTIONS, AND THE RISE OF LARGE FARMS","year":2018,"lang":"en","type":"article","venue":"International Economic Review","topic":"Agricultural Economics and Policy","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Dominance (genetics); Subsidy; Economics; Liberian dollar; Profit maximization; Agricultural economics; Agriculture; Productivity; Profit (economics); Distribution (mathematics); Microeconomics; Market economy; Geography; Economic growth; Mathematics","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":[0.0002386668,0.00006592483,0.0001653182,0.00001208839,0.000072006,0.00001644779,0.0002783868,0.00003112079,0.0006212667],"category_scores_gemma":[0.00005990063,0.00001965658,0.00007428907,0.00006247821,0.0002305665,0.00006403973,0.0001028352,0.00004262668,0.0001273102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003947174,"about_ca_system_score_gemma":0.000008978342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004104744,"about_ca_topic_score_gemma":0.0003649429,"domain_scores_codex":[0.9994793,0.00001983702,0.0002634514,0.0001206682,0.00002602647,0.00009074015],"domain_scores_gemma":[0.99966,0.00005365488,0.000175518,0.00004595414,0.00003839011,0.00002646395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002050363,0.00003016808,0.004416783,0.00003260252,0.00007662924,1.997319e-7,0.00003972477,3.500908e-7,0.0003673483,0.7424976,0.01332393,0.2391941],"study_design_scores_gemma":[0.0001586262,0.000025015,0.009649807,0.00009040831,0.00000947122,0.00001285182,0.0000234696,0.00006698892,0.00005258236,0.005521232,0.9843258,0.00006372725],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8019011,0.02185187,0.000001621195,0.1579375,0.000278247,0.0003449167,0.0002346813,0.00002445104,0.01742566],"genre_scores_gemma":[0.9563273,0.04096743,0.0000157573,0.001487965,0.000618423,0.00002283862,0.00001763204,4.839995e-7,0.0005421343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9710019,"threshold_uncertainty_score":0.6802434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243062525214813,"score_gpt":0.2729966727976274,"score_spread":0.2605660475454793,"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."}}