{"id":"W2904852339","doi":"10.3968/10687","title":"Reflection and Improvement of Litigation Relief in China's Administrative Contract Dispute Resolution","year":2018,"lang":"en","type":"article","venue":"Canadian social science","topic":"Conflict of Laws and Jurisdiction","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normative; Vagueness; Scope (computer science); Interpretation (philosophy); Dispute resolution; Administrative law; Judicial interpretation; Law and economics; Law; Business; Ambiguity; Political science; Sociology; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01269534,0.000224784,0.0003833641,0.002722414,0.00626611,0.006678411,0.002239832,0.002088116,0.00366511],"category_scores_gemma":[0.0166566,0.0002824587,0.0005545949,0.002169797,0.00344383,0.003904895,0.005653828,0.002213156,0.0001859686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0109618,"about_ca_system_score_gemma":0.03035807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03418437,"about_ca_topic_score_gemma":0.03436301,"domain_scores_codex":[0.9883201,0.004198013,0.0008148502,0.0007532332,0.003855529,0.002058293],"domain_scores_gemma":[0.9961349,0.001561137,0.0005299432,0.0002204742,0.00106947,0.0004840716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001145296,0.0003935894,0.03461403,0.0004571459,0.00004064612,0.001900393,0.0610206,0.003949881,0.004206096,0.5611188,0.01335626,0.3188281],"study_design_scores_gemma":[0.0003464872,0.0007173138,0.1739415,0.001181052,0.0003611572,0.00160293,0.1591868,0.07140929,0.01063252,0.2527278,0.3275409,0.0003523358],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7896794,0.004100709,0.01908517,0.03103643,0.0003329344,0.0006117595,0.0000436513,0.0001497711,0.1549601],"genre_scores_gemma":[0.9897761,0.0004734375,0.002662752,0.0006366097,0.00005546229,0.00005070684,0.00001769747,0.000006420514,0.006320765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03418437,"threshold_uncertainty_score":0.07953382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02187633741211708,"score_gpt":0.3365631970575533,"score_spread":0.3146868596454362,"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."}}