{"id":"W2795529249","doi":"10.1007/s11192-018-2732-8","title":"Can Twitter increase the visibility of Chinese publications?","year":2018,"lang":"en","type":"article","venue":"Scientometrics","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; McGill University","funders":"Fonds de Recherche du Québec-Société et Culture; Andrew W. Mellon Foundation","keywords":"Visibility; Social media; Citation; China; Political science; Library science; Computer science; Geography; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.003438503,0.0003303149,0.0004613503,0.005057088,0.001971518,0.006006909,0.0007313535,0.001302859,0.01345444],"category_scores_gemma":[0.02797533,0.0002149343,0.0005185992,0.00747478,0.001489191,0.008483963,0.002577908,0.001048936,0.001199943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002252685,"about_ca_system_score_gemma":0.002883464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01606612,"about_ca_topic_score_gemma":0.01416317,"domain_scores_codex":[0.9975484,0.0007423538,0.0001490118,0.0003002059,0.0007465055,0.0005134547],"domain_scores_gemma":[0.9716395,0.01378036,0.006157314,0.001542091,0.004176702,0.002704088],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008742218,0.000304175,0.7140492,0.001037719,0.0003799386,0.001195332,0.01815235,0.0009025139,0.00282451,0.03531836,0.03312455,0.1918372],"study_design_scores_gemma":[0.0001534815,0.0004496925,0.8381357,0.0005784509,0.0007258838,0.0004899522,0.02575452,0.008972548,0.004666322,0.02541066,0.09451679,0.0001459944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8727449,0.005749926,0.001313059,0.02612841,0.0008829494,0.00005788395,0.002245685,0.0002187513,0.09065838],"genre_scores_gemma":[0.9949934,0.001098383,0.0001535457,0.0003852529,0.0005287593,0.00001308579,0.0002049975,0.00001617783,0.002606379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9965615,"threshold_uncertainty_score":0.04500955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5383689152925458,"score_gpt":0.5912490065647195,"score_spread":0.05288009127217363,"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."}}