{"id":"W7126593479","doi":"","title":"RELATIONSHIP-BASED LENDING FOR DIVERSIFIED AGRICULTURE SYSTEMS IN CANADA: HOW TRUST AND COMMUNITY MAY TRANSFORM FINANCIALIZED AND GENDERED FOOD SYSTEMS","year":2021,"lang":"en","type":"other","venue":"eCommons (Cornell University)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Incentive; Food systems; Snowball sampling; Food security; Work (physics); Multinational corporation; Asset (computer security); Agribusiness","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004613698,0.0006524048,0.00112384,0.001192099,0.0007299109,0.0001583109,0.0004984119,0.000672969,0.00004142441],"category_scores_gemma":[0.00009939221,0.000734337,0.0001488586,0.001277643,0.0001413534,0.000164604,0.0001353476,0.001113933,0.000003228038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002615661,"about_ca_system_score_gemma":0.001089534,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6759223,"about_ca_topic_score_gemma":0.9751374,"domain_scores_codex":[0.9970408,0.001149754,0.0003136673,0.0006757557,0.000209716,0.0006103136],"domain_scores_gemma":[0.9977838,0.0007284884,0.0004721177,0.000631092,0.0001056375,0.0002788791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001427044,0.001000678,0.2578692,0.01725269,0.002415489,0.00115998,0.003083067,0.0101228,0.0002025168,0.045269,0.6600661,0.0001314059],"study_design_scores_gemma":[0.02574032,0.000498636,0.06373021,0.004679569,0.001909142,0.00009961824,0.0642257,0.009266236,0.00003672773,0.0001007867,0.8254183,0.004294788],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3247217,0.0192396,0.003984102,0.0004691605,0.003789281,0.01665991,0.04658531,0.001156966,0.5833939],"genre_scores_gemma":[0.8818266,0.00007374062,0.00006068841,0.00000716468,0.0000639436,0.00001573235,0.001247469,0.0002924249,0.1164123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5571048,"threshold_uncertainty_score":0.9995108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07072478350322359,"score_gpt":0.1953092606609733,"score_spread":0.1245844771577497,"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."}}