{"id":"W1934957961","doi":"10.3968/j.ccc.1923670020130902.1915","title":"The Manufacture-Learning-Research Cooperation Policy Research in China","year":2013,"lang":"en","type":"article","venue":"Cross-cultural communication","topic":"Research, Science, and Academia","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scope (computer science); China; Government (linguistics); Work (physics); Business; Policy learning; Mode (computer interface); Phase (matter); Industrial organization; Knowledge management; Engineering; Political science; Computer science; Mechanical engineering","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":["metaresearch","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch","sts"],"category_scores_codex":[0.03653271,0.0001590732,0.0002028244,0.0007100411,0.006831336,0.009966661,0.006089617,0.0001938511,0.0002917471],"category_scores_gemma":[0.02521701,0.00008248063,0.00008025017,0.005184977,0.004191161,0.003269301,0.001547863,0.003448055,0.003418092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004093638,"about_ca_system_score_gemma":0.0003328543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005922609,"about_ca_topic_score_gemma":0.0007918497,"domain_scores_codex":[0.9880382,0.005397405,0.0008148749,0.00059012,0.004045098,0.001114272],"domain_scores_gemma":[0.9869015,0.006216457,0.0001617803,0.002018427,0.00445417,0.0002476538],"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":[0.0001526862,0.0002007533,0.06804089,0.00001558324,0.00001515586,0.000003229441,0.01551068,0.003271577,0.02928255,0.1254967,0.1272157,0.6307946],"study_design_scores_gemma":[0.000407736,0.0001012883,0.5323235,0.00003625683,5.088724e-7,0.000005354107,0.006237363,0.00993243,0.002704426,0.1325952,0.3154616,0.0001943113],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9131585,0.0006910627,0.00001324011,0.0438442,0.00006829148,0.0008299393,0.000001847436,0.00004231713,0.04135055],"genre_scores_gemma":[0.974196,0.001270974,0.000089922,0.00008438349,0.0001759245,0.0002280315,0.00002011719,0.00001152113,0.02392312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6306003,"threshold_uncertainty_score":0.9992879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.242543601359386,"score_gpt":0.5673676853482557,"score_spread":0.3248240839888696,"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."}}