{"id":"W1941175482","doi":"10.1002/asi.23418","title":"Dimensions and uncertainties of author citation rankings: Lessons learned from frequency‐weighted in‐text citation counting","year":2015,"lang":"en","type":"article","venue":"Journal of the Association for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Citation; Weighting; Computer science; Rank (graph theory); Citation analysis; Citation impact; Field (mathematics); Subject (documents); Information retrieval; Statistics; Scheme (mathematics); Mathematics; Data science; Library science; Combinatorics; 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":["metaresearch","bibliometrics"],"domain":"evaluation","study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics","metaresearch"],"domain":"evaluation","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.04363978,0.001143004,0.002074398,0.008248032,0.001816164,0.008365721,0.002650182,0.00211258,0.001705006],"category_scores_gemma":[0.3039082,0.0007862126,0.001145588,0.008725614,0.004723475,0.01425965,0.004363407,0.003938911,0.0003624209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00275895,"about_ca_system_score_gemma":0.001843744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003531771,"about_ca_topic_score_gemma":0.002250632,"domain_scores_codex":[0.9742746,0.01435493,0.001876424,0.002482859,0.006371406,0.0006398366],"domain_scores_gemma":[0.738965,0.2172143,0.01351246,0.01471409,0.0141428,0.001451229],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000262545,0.0001688614,0.04029947,0.0006522638,0.0006191513,0.0001961846,0.003214844,0.1213294,0.0008570779,0.5802199,0.00361805,0.2485623],"study_design_scores_gemma":[0.0000222068,0.00005243145,0.005258601,0.0001445816,0.00006232067,0.00007575754,0.0004518186,0.1775684,0.0006507596,0.8127049,0.002911556,0.00009665052],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2145803,0.005458273,0.7561494,0.006247722,0.0004412408,0.0001265569,0.0004698901,0.0002642255,0.01626232],"genre_scores_gemma":[0.8806632,0.001751133,0.1145701,0.0004351836,0.0008684639,0.0001416597,0.0003154904,0.0001217625,0.001133081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.991752,"threshold_uncertainty_score":0.230792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3682621630265268,"score_gpt":0.492080593318006,"score_spread":0.1238184302914793,"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."}}