{"id":"W4251832911","doi":"10.46877/manzi.2019.15","title":"Fighting against or Coexisting with Drought? Conviviality, Inequality and Peasant Mobility in Northeast Brazil","year":2019,"lang":"en","type":"report","venue":"","topic":"Agriculture, Land Use, Rural Development","field":"Agricultural and Biological Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Universität zu Köln; Freie Universität Berlin; Universidade de São Paulo; Universidad Nacional de La Plata; Consejo Nacional de Investigaciones Científicas y Técnicas; Bundesministerium für Bildung und Forschung; Université Laval","keywords":"Peasant; Context (archaeology); Inequality; Politics; State (computer science); Intervention (counseling); Geography; Period (music); Psychological intervention; Development economics; Political science; Political economy; Economy; Sociology; Economic growth; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001612766,0.0006349731,0.001056366,0.00002432272,0.0002515423,0.0001872487,0.0003595809,0.0004735943,0.0003430499],"category_scores_gemma":[0.000340514,0.0001650392,0.0001125805,0.0005350649,0.0001172363,0.0001858208,0.0004086438,0.0006184273,0.00002518198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003144495,"about_ca_system_score_gemma":0.0002680203,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006182004,"about_ca_topic_score_gemma":0.05639973,"domain_scores_codex":[0.9959096,0.0002275599,0.001012894,0.001125245,0.001058148,0.0006665691],"domain_scores_gemma":[0.9980828,0.0005308037,0.0005840835,0.0001604636,0.0004243444,0.0002175679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000212693,0.0003374557,0.9549381,0.0005976673,0.00008987372,0.0001155819,0.000258029,0.00000642436,0.000120247,0.0001088138,0.002853466,0.04036171],"study_design_scores_gemma":[0.0003812829,0.000221722,0.9595246,0.0006731893,0.00002905724,0.00008119914,0.0007182952,0.00002986792,0.00005732964,0.0001530759,0.03721796,0.0009124191],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9728996,0.0001977589,0.000002187659,0.000268037,0.0002662581,0.00107321,0.0002160008,0.0001307085,0.02494627],"genre_scores_gemma":[0.9908061,0.0004324182,0.0001389556,0.0002876054,0.0004601825,0.0000615086,0.0008443179,0.000004594534,0.006964291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05021773,"threshold_uncertainty_score":0.9608185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0548561629058913,"score_gpt":0.282093349050461,"score_spread":0.2272371861445697,"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."}}