{"id":"W3205355219","doi":"10.1002/pra2.525","title":"A Bibliometric Analysis of the Annual Meeting Proceedings of the Association for Information Science and Technology","year":2021,"lang":"en","type":"article","venue":"Proceedings of the Association for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Association (psychology); Information science; Library science; Social media; China; Point (geometry); Bibliometrics; Data science; Sociology; Political science; Computer science; Psychology; World Wide Web; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.009521699,0.0005108215,0.0009018046,0.09259368,0.001867558,0.005781452,0.0009964501,0.0006507104,0.006213199],"category_scores_gemma":[0.06544702,0.0001983472,0.0008541328,0.1433061,0.000478915,0.003202543,0.001627702,0.0005872558,0.002773648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002451893,"about_ca_system_score_gemma":0.003849213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005875498,"about_ca_topic_score_gemma":0.00734696,"domain_scores_codex":[0.9822681,0.002620994,0.002862898,0.0009102983,0.01080238,0.0005352843],"domain_scores_gemma":[0.9428455,0.0188618,0.0122101,0.002733452,0.02150066,0.001848455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002628387,0.0002737999,0.4970619,0.00413223,0.0009795418,0.0005788917,0.0036251,0.001738272,0.002118334,0.008705436,0.1061671,0.3743565],"study_design_scores_gemma":[0.00002071072,0.0001324061,0.8128063,0.0007622428,0.0004516109,0.0007752933,0.004716564,0.004232988,0.00172416,0.00218606,0.1721105,0.00008098195],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6896336,0.037428,0.006210862,0.006289486,0.00216998,0.0005629339,0.07434484,0.0009333767,0.182427],"genre_scores_gemma":[0.9413546,0.01323532,0.004637353,0.0002118478,0.001467726,0.0004137443,0.02813875,0.0001517175,0.01038898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9074063,"threshold_uncertainty_score":0.05035615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07438405260671334,"score_gpt":0.4090650494434592,"score_spread":0.3346809968367458,"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."}}