{"id":"W2521174906","doi":"10.1515/ajle-2016-0015","title":"Assessing Personal Injury Liabilities in China from National to Provincial Level: An International Comparative Analysis","year":2016,"lang":"en","type":"article","venue":"Asian Journal of Law and Economics","topic":"Legal principles and applications","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Tort; Personal injury; Damages; Convergence (economics); Mainland China; Civil law (Civil law); China; Compensation (psychology); Law; Common law; Political science; Comparative law; Delict; Economics; Law and economics; Liability; Public law; Private law; Black letter law; Psychology; Macroeconomics","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":[0.00164166,0.000218657,0.0004414652,0.006675967,0.001569545,0.001093793,0.0005445788,0.0002876156,0.002516056],"category_scores_gemma":[0.003187999,0.0001572271,0.0007328098,0.01118092,0.0009036034,0.0005745881,0.001141359,0.000409392,0.0001134772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006922095,"about_ca_system_score_gemma":0.003742593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3956041,"about_ca_topic_score_gemma":0.5052975,"domain_scores_codex":[0.9989312,0.0002719429,0.00009546823,0.000129933,0.0002964311,0.0002749329],"domain_scores_gemma":[0.9973391,0.0005729251,0.0006159936,0.0001812462,0.00103532,0.0002554154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000095018,0.00009729403,0.9750285,0.000169935,0.000321275,0.0005682289,0.003429795,0.001571499,0.0004109235,0.002836929,0.0003942914,0.01507628],"study_design_scores_gemma":[0.000003642247,0.00006082593,0.9937307,0.00003913063,0.0001176474,0.00009269356,0.003756096,0.001039925,0.0001261336,0.0001441803,0.0008790671,0.000009939537],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974287,0.0002768894,0.0001273014,0.00005328036,0.000003157201,0.00001194078,0.0001833882,0.000002254706,0.001913053],"genre_scores_gemma":[0.9993274,0.0001193216,0.0001114955,0.000007246845,0.000001835371,0.000005027566,0.0001926584,5.340495e-7,0.00023462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3956041,"threshold_uncertainty_score":0.7866029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06799291285301286,"score_gpt":0.3682331806633817,"score_spread":0.3002402678103688,"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."}}