{"id":"W1996965557","doi":"10.1002/meet.1450440262","title":"All‐author vs. first‐author co‐citation analysis of the Information Science field using Scopus","year":2007,"lang":"en","type":"article","venue":"Proceedings of the American Society for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Citation; Co-citation; Computer science; Field (mathematics); Citation analysis; Information retrieval; Data science; Mathematics; Library science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","sts"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.03121394,0.0001151252,0.0003217617,0.02735054,0.00124874,0.0009065881,0.003470565,0.00009169534,0.00000370749],"category_scores_gemma":[0.04130895,0.00006639062,0.0002536096,0.3332235,0.005848847,0.007111587,0.000922472,0.0001976002,0.000001831874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002189921,"about_ca_system_score_gemma":0.0004959635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001029781,"about_ca_topic_score_gemma":0.00000383198,"domain_scores_codex":[0.9922478,0.000004624692,0.0009947715,0.0002655262,0.005926056,0.0005612829],"domain_scores_gemma":[0.9837686,0.0008146413,0.002214182,0.0003785149,0.01269607,0.0001279591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001215717,0.00008105279,0.4802863,0.0001045739,0.0001814694,1.985298e-8,0.01051232,0.0002653413,0.06445412,0.05526457,0.005996168,0.3827325],"study_design_scores_gemma":[0.0006414678,0.0005571137,0.4037954,0.00004478172,0.0002343873,0.00001087633,0.057763,0.1833401,0.3201043,0.006130435,0.02698224,0.0003959138],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865288,0.00001270678,0.006973148,0.005101746,0.000151297,0.0004493332,0.00002516978,0.00002101347,0.0007367675],"genre_scores_gemma":[0.9927917,0.00002622399,0.00643497,0.0007147756,0.000007118848,0.000009107684,7.085925e-7,0.000001958944,0.00001343198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3823366,"threshold_uncertainty_score":0.9975691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2087072117023929,"score_gpt":0.5037997684738248,"score_spread":0.2950925567714319,"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."}}