{"id":"W4413183374","doi":"10.36956/rwae.v6i3.2410","title":"Trends in Agricultural Products Marketing: A Bibliometric Analysis and Future Research Agenda","year":2025,"lang":"en","type":"article","venue":"Research on World Agricultural Economy","topic":"Digitalization and Economic Development in Agriculture","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Gandhi Institute of Technology and Management","keywords":"Agriculture; Regional science; Marketing research; Marketing; Agricultural marketing; Business; Bibliometrics; Agricultural economics; Economics; Marketing management; Geography; Computer science; Library science; Relationship marketing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01712905,0.0006247471,0.001750408,0.1253327,0.001688958,0.01307904,0.00106048,0.0008913025,0.003944172],"category_scores_gemma":[0.04676811,0.000308471,0.001392341,0.2368645,0.001324822,0.009911072,0.002204933,0.0007295005,0.0006585349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00480547,"about_ca_system_score_gemma":0.008579144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007339904,"about_ca_topic_score_gemma":0.01108199,"domain_scores_codex":[0.9877601,0.003358325,0.00205891,0.0008245695,0.005443682,0.0005544629],"domain_scores_gemma":[0.9449014,0.03582137,0.006776103,0.001847838,0.009953162,0.0007000994],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001605647,0.000218646,0.1826362,0.02493091,0.0009835393,0.0005455076,0.009179063,0.002102094,0.00141413,0.04502293,0.02001246,0.712794],"study_design_scores_gemma":[0.00004520648,0.0003437347,0.5156121,0.02501342,0.002002266,0.001858415,0.06073109,0.01627383,0.002414828,0.05750388,0.3179681,0.0002332086],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4967905,0.2794832,0.02726997,0.05989401,0.001304264,0.00181671,0.02962847,0.0008189435,0.102994],"genre_scores_gemma":[0.7916291,0.1568435,0.03035815,0.001089456,0.001184212,0.001037145,0.01439336,0.00011588,0.003349188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9828709,"threshold_uncertainty_score":0.09058815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06307759891057335,"score_gpt":0.3291927338854617,"score_spread":0.2661151349748883,"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."}}