{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006770534,0.0005084448,0.0009957056,0.02902501,0.001133439,0.00496604,0.0007363819,0.0006174449,0.00243447],"category_scores_gemma":[0.05696406,0.000152861,0.0009842362,0.03040784,0.0007793827,0.002878893,0.001733557,0.0008165155,0.0005001489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001329463,"about_ca_system_score_gemma":0.001230779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007712123,"about_ca_topic_score_gemma":0.00901371,"domain_scores_codex":[0.9940699,0.002378838,0.0006425855,0.0004583194,0.002134012,0.0003163543],"domain_scores_gemma":[0.9094255,0.06419828,0.006739282,0.004267898,0.01418109,0.001187974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001855572,0.0006809437,0.5698031,0.001743521,0.002644483,0.0004235283,0.002848587,0.04491229,0.009990012,0.04108077,0.009812478,0.3142047],"study_design_scores_gemma":[0.0001556808,0.0006985757,0.4322123,0.0003524699,0.001020135,0.0004523245,0.005355964,0.4628439,0.04034299,0.04811766,0.008149061,0.0002990284],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9473029,0.001311233,0.03411691,0.000415227,0.0001260944,0.0001077642,0.002268253,0.000737504,0.01361404],"genre_scores_gemma":[0.9849741,0.0002532496,0.01296706,0.00002010899,0.0000422011,0.00005584205,0.0008216391,0.00006861723,0.0007971422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.970975,"threshold_uncertainty_score":0.03580642,"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."}}