{"id":"W4281628477","doi":"10.5539/jpl.v15n3p1","title":"Contextualizing Relations Between Presumptions and Legal Fictions: An Analysis of the Chinese Civil Code","year":2022,"lang":"en","type":"article","venue":"Journal of Politics and Law","topic":"Law and Political Science","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Persuasion; Premise; Statutory law; Law; Scope (computer science); Civil procedure; Political science; Law and economics; Set (abstract data type); Legal research; Epistemology; Psychology; Sociology; Computer science; Social psychology; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007793418,0.0000385009,0.0001366336,0.00006276834,0.001317377,0.00006194792,0.0001466666,0.00002219627,0.0001645794],"category_scores_gemma":[0.0001620905,0.00002595293,0.00008105575,0.0003638565,0.0004838333,0.0001775118,0.00005474278,0.0001474,1.303225e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004425886,"about_ca_system_score_gemma":0.000104181,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007067313,"about_ca_topic_score_gemma":0.01146479,"domain_scores_codex":[0.9989958,0.0002666552,0.0002387869,0.00005794387,0.0002989472,0.0001419126],"domain_scores_gemma":[0.9991817,0.0003517238,0.0001481058,0.00007461324,0.00008532895,0.0001585533],"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":[7.100269e-7,0.00001664623,0.1461895,0.000001029876,0.00004600547,4.43439e-7,0.00168325,0.0001120742,0.000008544385,0.8518872,0.00003480146,0.00001974543],"study_design_scores_gemma":[0.0002033679,0.0001068759,0.8428963,0.000009762829,0.0005714792,0.000009020374,0.003917947,0.0005269923,0.000005327728,0.04920042,0.1024625,0.00008996663],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9775462,0.0001209514,0.00009763955,0.003011874,0.0001013742,0.0000402627,0.0001538165,0.000002546924,0.01892531],"genre_scores_gemma":[0.9989148,0.00001471447,0.00004871796,0.0002367585,0.0001142915,8.271765e-7,9.85018e-7,0.000001745406,0.0006671573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8026868,"threshold_uncertainty_score":0.9999828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02994593052405136,"score_gpt":0.3468138678014936,"score_spread":0.3168679372774423,"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."}}