{"id":"W1530517025","doi":"10.1002/asi.23027","title":"The knowledge base and research front of information science 2006–2010: An author cocitation and bibliographic coupling analysis","year":2014,"lang":"en","type":"article","venue":"Journal of the Association for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Webometrics; Bibliographic coupling; Knowledge base; Scientometrics; Citation analysis; Front (military); Web of science; Data science; Computer science; Bibliometrics; Information science; Period (music); Network analysis; Citation; Library science; World Wide Web; Political science; MEDLINE; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006923731,0.0003000025,0.0007647204,0.05248857,0.001620432,0.007215838,0.000766119,0.0007138943,0.005514368],"category_scores_gemma":[0.04360193,0.0002427285,0.000837502,0.05326674,0.00111373,0.005109558,0.004754812,0.001268624,0.001569449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004140013,"about_ca_system_score_gemma":0.003552383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02494559,"about_ca_topic_score_gemma":0.01988599,"domain_scores_codex":[0.9945047,0.0006569278,0.0006531834,0.0004763097,0.003033201,0.0006755475],"domain_scores_gemma":[0.9157737,0.03355738,0.01957822,0.003349145,0.02259528,0.005146306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004829128,0.0001801982,0.9121295,0.0005041435,0.0003119192,0.0002965316,0.01239057,0.001255445,0.001514279,0.009779553,0.004435233,0.05671966],"study_design_scores_gemma":[0.00001251271,0.00007132629,0.9765714,0.00012014,0.0001052354,0.0001594555,0.006100917,0.001799685,0.0006602032,0.001521511,0.0128494,0.00002824093],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9598074,0.001383594,0.001591372,0.0007622348,0.00004393576,0.00006176867,0.004577554,0.00006080912,0.03171127],"genre_scores_gemma":[0.988801,0.0007229256,0.001450462,0.00009142262,0.00009624691,0.00005568671,0.006134439,0.00004921116,0.00259848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9475114,"threshold_uncertainty_score":0.04960078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2321609182054428,"score_gpt":0.5205154533318411,"score_spread":0.2883545351263983,"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."}}