{"id":"W4225140398","doi":"10.1007/s11024-022-09468-7","title":"China’s Research Evaluation Reform: What are the Consequences for Global Science?","year":2022,"lang":"en","type":"article","venue":"Minerva","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bureau de Coopération Interuniversitaire; Université du Québec à Montréal; Université de Montréal","funders":"Canada Research Chairs","keywords":"China; Science Citation Index; Political science; Science policy; Coronavirus disease 2019 (COVID-19); Higher education; Citation; Citation impact; Position (finance); Public administration; Economic growth; Economics; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch","bibliometrics"],"domain":"evaluation","study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["metaresearch"],"domain":"evaluation","study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","sts","scholarly_communication","open_science"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.2077743,0.0001102278,0.0001918551,0.01718655,0.004162999,0.007183183,0.006138437,0.0000393037,0.0008530358],"category_scores_gemma":[0.06150865,0.00005952724,0.0001280957,0.2462188,0.001824293,0.001553982,0.002265556,0.0003573434,0.00008656083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001766082,"about_ca_system_score_gemma":0.001920539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000883209,"about_ca_topic_score_gemma":0.0001569007,"domain_scores_codex":[0.9625664,0.001059478,0.000540063,0.0009641164,0.03383564,0.001034318],"domain_scores_gemma":[0.9863641,0.002234629,0.0002608425,0.001236516,0.009595339,0.0003085965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009742548,0.0002158198,0.01319842,0.000005593078,0.00001523988,0.00001077309,0.0006745189,0.0007099098,0.002286364,0.02067651,0.05451056,0.9075989],"study_design_scores_gemma":[0.001274749,0.001014008,0.359382,0.00001892009,0.00001757785,0.00009145323,0.07495495,0.1230683,0.001209156,0.2894324,0.1491544,0.0003820147],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9241681,0.004845029,0.0002335572,0.05024822,0.00214486,0.001469453,0.0001073241,0.00002067584,0.01676284],"genre_scores_gemma":[0.9964975,0.0000733854,0.0002234567,0.0002638144,0.0001151317,0.0003969983,0.000005528967,0.000005295293,0.002418858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9072168,"threshold_uncertainty_score":0.9992388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8411194510935742,"score_gpt":0.6756913006516972,"score_spread":0.165428150441877,"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."}}