{"id":"W1833497418","doi":"","title":"Study on the Adjustment Procedures of Administrative Division in China: From The Perspective of Text Analysis","year":2015,"lang":"en","type":"article","venue":"Canadian social science","topic":"Education, Law, and Society","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Administrative law; Legislation; Administrative division; Plenary session; Legislature; Order (exchange); Element (criminal law); Public administration; China; Political science; Quality (philosophy); Law; Law and economics; Business; Economics; 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":[0.01034637,0.0002739856,0.0003153398,0.005692362,0.003608561,0.002500349,0.001408863,0.0005912601,0.002442585],"category_scores_gemma":[0.02480627,0.0002488578,0.0004109179,0.006879291,0.002621385,0.004326229,0.001320671,0.001190568,0.0002455342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0102265,"about_ca_system_score_gemma":0.01322628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06374557,"about_ca_topic_score_gemma":0.07914583,"domain_scores_codex":[0.9894915,0.003154369,0.001295765,0.001212443,0.003959345,0.0008866212],"domain_scores_gemma":[0.9832329,0.007923874,0.002805582,0.0008528904,0.004754778,0.0004299592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001313127,0.0001410886,0.2991826,0.001356408,0.00007205906,0.002598021,0.314581,0.001338353,0.004666262,0.1446548,0.01477922,0.2164987],"study_design_scores_gemma":[0.00003094947,0.0001010458,0.6989954,0.0007925992,0.0001446015,0.0005335439,0.1598635,0.01572491,0.00607933,0.01975677,0.09779475,0.0001826095],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9554899,0.001291911,0.01328931,0.004198027,0.0001603213,0.0004840107,0.0008364565,0.00008562112,0.02416441],"genre_scores_gemma":[0.9876772,0.0006900309,0.004630747,0.0002578081,0.00008108301,0.0001861025,0.0006368553,0.00003605721,0.005804172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06374557,"threshold_uncertainty_score":0.126749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06926417847958788,"score_gpt":0.3899153309054424,"score_spread":0.3206511524258546,"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."}}