{"id":"W4407665777","doi":"10.1080/00130095.2025.2460386","title":"China’s Vulnerability Paradox","year":2025,"lang":"en","type":"article","venue":"Economic Geography","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"China; Vulnerability (computing); Political science; Geography; Computer science; Law; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000747672,0.0001931412,0.0003005739,0.001112517,0.002372802,0.001633827,0.0003811952,0.0007400322,0.003425229],"category_scores_gemma":[0.001221327,0.00008491301,0.0002430611,0.00146407,0.002611922,0.001204147,0.002346131,0.001002937,0.0001621028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004141391,"about_ca_system_score_gemma":0.004556179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06051288,"about_ca_topic_score_gemma":0.06121936,"domain_scores_codex":[0.9997224,0.00004350735,0.000008635376,0.00004510796,0.00006239335,0.0001178898],"domain_scores_gemma":[0.999501,0.00008527263,0.0000824985,0.00006078442,0.0001250252,0.0001455536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0003281449,0.00006901988,0.1023749,0.0002886085,0.0002487277,0.002692414,0.009553592,0.006426882,0.002442907,0.7203663,0.06419589,0.09101254],"study_design_scores_gemma":[0.000138397,0.0001653216,0.3715701,0.0002254559,0.000174418,0.0009040796,0.01016593,0.008151976,0.001151473,0.3993997,0.2077987,0.0001544891],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8167262,0.004541237,0.0007093846,0.08030229,0.0002006899,0.00001711327,0.0007539163,0.00005751945,0.09669159],"genre_scores_gemma":[0.995415,0.0007307415,0.00006338027,0.001874302,0.00005113353,0.000005596584,0.0000719399,0.000004425026,0.001783486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06051288,"threshold_uncertainty_score":0.1203213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004079087059784874,"score_gpt":0.2033317180755375,"score_spread":0.1992526310157526,"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."}}