{"id":"W3123946626","doi":"10.2139/ssrn.3167181","title":"Can Media and Text Analytics Provide Insights into Labour Market Conditions in China?","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Government of Canada; Bank of Canada","funders":"","keywords":"China; Analytics; Data science; Business; Social media; Industrial organization; Political science; Computer science; World Wide Web; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006194733,0.0002393822,0.0002513152,0.003784034,0.0006547047,0.001771543,0.0002905138,0.0003421493,0.003433802],"category_scores_gemma":[0.001792598,0.0001077234,0.0002518606,0.00410447,0.0004864392,0.001747907,0.0006419062,0.0003387136,0.0004221775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00121385,"about_ca_system_score_gemma":0.001403753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04373944,"about_ca_topic_score_gemma":0.06338347,"domain_scores_codex":[0.9996436,0.000056851,0.0000305846,0.00004715042,0.000106678,0.0001150951],"domain_scores_gemma":[0.9985204,0.0003297036,0.0005448495,0.00004264121,0.0003562275,0.0002061476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001286389,0.00006165504,0.9473373,0.0001744343,0.00008818009,0.0006342606,0.004297433,0.0006734153,0.002300715,0.003594356,0.004837659,0.03587201],"study_design_scores_gemma":[0.000004784891,0.0000376035,0.9848424,0.00004057654,0.00002832626,0.0000357344,0.005728494,0.002998449,0.0004341421,0.0009719951,0.004858783,0.00001858812],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899344,0.0004829662,0.0002173482,0.001415603,0.0000390004,0.00001458678,0.001566599,0.0000136217,0.006316009],"genre_scores_gemma":[0.9974381,0.0003093765,0.00009078978,0.00008102573,0.00007841114,0.000009328925,0.0006504837,0.000003744573,0.001338833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04373944,"threshold_uncertainty_score":0.08696967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006913561388059448,"score_gpt":0.2454083528805243,"score_spread":0.2384947914924649,"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."}}