{"id":"W2055778626","doi":"10.1007/s11192-009-0127-6","title":"Citer analysis as a measure of research impact: library and information science as a case study","year":2009,"lang":"en","type":"article","venue":"Scientometrics","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Citation; Measure (data warehouse); Exploratory research; Information science; Citation analysis; Psychology; Field (mathematics); Computer science; Information retrieval; Library science; Sociology; Social science; Mathematics; Data mining","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.02063207,0.0008557229,0.001405375,0.0427901,0.00268527,0.009721202,0.00165452,0.002543971,0.004599466],"category_scores_gemma":[0.05741834,0.0003185272,0.001771063,0.04877802,0.002662167,0.007392117,0.002386493,0.001376261,0.0008851545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00452494,"about_ca_system_score_gemma":0.003636868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005712051,"about_ca_topic_score_gemma":0.00837749,"domain_scores_codex":[0.9721224,0.01427716,0.001497089,0.001000005,0.0103569,0.0007463603],"domain_scores_gemma":[0.8828311,0.09328696,0.007359065,0.003070914,0.01145306,0.001998968],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001038425,0.003609592,0.4646136,0.003032131,0.00137433,0.003389451,0.01634432,0.0188781,0.01338339,0.1046621,0.008537301,0.3611373],"study_design_scores_gemma":[0.0003131696,0.003995615,0.5522988,0.001161726,0.002655222,0.007444895,0.04930792,0.2123009,0.035583,0.08529421,0.04881737,0.0008271167],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.826115,0.00703699,0.05243288,0.004579157,0.0002623105,0.0006590858,0.003012625,0.0005804684,0.1053216],"genre_scores_gemma":[0.9694803,0.001110003,0.02582829,0.00007295603,0.000111284,0.0001887489,0.0004524717,0.00008632164,0.002669622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9793679,"threshold_uncertainty_score":0.1091141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5600534252037995,"score_gpt":0.6469165712479984,"score_spread":0.08686314604419887,"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."}}