{"id":"W2042501957","doi":"10.1111/j.1755-618x.2003.tb00256.x","title":"Making Food Count: Expert Knowledge and Global Technologies of Government*","year":2003,"lang":"fr","type":"article","venue":"Canadian Review of Sociology/Revue canadienne de sociologie","topic":"Global trade, sustainability, and social impact","field":"Business, Management and Accounting","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Political science; Humanities; Globalization; Government (linguistics); Agriculture; Sociology; Geography; Philosophy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.015115,0.0003879925,0.0004707199,0.004141273,0.003434373,0.01138488,0.001272033,0.003225354,0.006750928],"category_scores_gemma":[0.03112643,0.0003092177,0.0004105344,0.002948279,0.0306991,0.01462367,0.005983072,0.002247331,0.0008814179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004770409,"about_ca_system_score_gemma":0.003821986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003962874,"about_ca_topic_score_gemma":0.004465405,"domain_scores_codex":[0.9884851,0.007477504,0.0003383964,0.001149985,0.001806638,0.0007422812],"domain_scores_gemma":[0.9618695,0.02992532,0.002363914,0.00304018,0.001943423,0.0008577034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001483103,0.00002195114,0.002776739,0.0001072572,0.00001189925,0.0001366415,0.01795441,0.001049232,0.0001368086,0.9162691,0.005184449,0.05633675],"study_design_scores_gemma":[0.000009091464,0.00002240273,0.002880195,0.0003151125,0.00001070177,0.000115362,0.01424308,0.001381323,0.0002913498,0.866526,0.1141815,0.000023836],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09855675,0.009115026,0.07274044,0.1031548,0.0005528105,0.00009723297,0.0001185587,0.000194859,0.7154696],"genre_scores_gemma":[0.9564991,0.005124973,0.01486576,0.003512577,0.0004335752,0.00009132837,0.00006088337,0.0000787133,0.01933309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9965656,"threshold_uncertainty_score":0.07993674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0513213802173435,"score_gpt":0.2872136795388603,"score_spread":0.2358922993215168,"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."}}