{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.0318094,0.00006322107,0.0002170342,0.02317482,0.0002766474,0.000629636,0.0007425679,0.0001361866,0.00000253261],"category_scores_gemma":[0.1345484,0.00004007589,0.00004012806,0.05246556,0.0003047743,0.003424509,0.0001914588,0.0001988307,0.000002529466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003260289,"about_ca_system_score_gemma":0.0005737058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006164548,"about_ca_topic_score_gemma":0.00002408044,"domain_scores_codex":[0.9944927,0.00007985512,0.0009347575,0.0001295627,0.00415492,0.0002081931],"domain_scores_gemma":[0.9823956,0.002030984,0.002130779,0.0001655309,0.01319598,0.00008105476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007602598,0.0000676298,0.6332604,0.00001194281,0.00003839547,3.74034e-7,0.009836748,0.0002804557,0.01113269,0.0936509,0.002941157,0.2487033],"study_design_scores_gemma":[0.002260648,0.0002984042,0.2459126,0.00006002048,0.00002602377,0.00000800958,0.02399545,0.02725129,0.005107863,0.6870221,0.007903318,0.0001542522],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797134,0.000151341,0.001700901,0.01733774,0.000416501,0.0001687151,0.00001987696,0.000006276552,0.0004852128],"genre_scores_gemma":[0.9977632,0.00008585454,0.002006853,0.00007488667,0.00001246339,0.000003389838,0.000001297253,0.000001657272,0.00005042363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5933713,"threshold_uncertainty_score":0.996956,"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."}}