{"id":"W4390018210","doi":"10.2139/ssrn.4655465","title":"Still Water Runs Deep: Soft Power in Chinese Prefectures and Municipalities","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Culture, Economy, and Development Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Soft power; Power (physics); Deep water; China; Geography; Agricultural economics; Environmental science; Business; Economics; Engineering; Marine engineering; Archaeology","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":[0.000367772,0.0001860737,0.0002489608,0.001622276,0.003799854,0.002445681,0.0006920854,0.000448878,0.004963319],"category_scores_gemma":[0.001591402,0.0002086231,0.0001770197,0.005097375,0.003646751,0.001582121,0.003073442,0.0006139668,0.0001471586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00514548,"about_ca_system_score_gemma":0.005911063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2813803,"about_ca_topic_score_gemma":0.5173224,"domain_scores_codex":[0.9994186,0.00007340912,0.00002488388,0.00005698785,0.00007191857,0.0003540902],"domain_scores_gemma":[0.9988534,0.0001687784,0.0002860135,0.00004182645,0.0001287759,0.0005212234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001202831,0.00008913981,0.7848375,0.00007196202,0.0000290853,0.001735801,0.1912738,0.0001933962,0.0004548157,0.008978901,0.001250166,0.01096514],"study_design_scores_gemma":[0.000004135507,0.00003263601,0.6269938,0.00002903003,0.00002011287,0.00007461059,0.3699083,0.0001545435,0.00005469596,0.0007125631,0.002003452,0.00001218398],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961227,0.00005984968,0.00001603512,0.0001544344,0.000002432109,0.000004576215,0.00003350725,9.625294e-7,0.003605668],"genre_scores_gemma":[0.9994159,0.00004282116,0.000007716682,0.00001705773,0.000001663766,0.000002945736,0.0000265744,7.875258e-7,0.0004845097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2813803,"threshold_uncertainty_score":0.559485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01091286173529953,"score_gpt":0.2755013102689238,"score_spread":0.2645884485336242,"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."}}