{"id":"W2953193560","doi":"10.1002/asi.21232","title":"The impact factor's Matthew Effect: A natural experiment in bibliometrics","year":2009,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":230,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Impact factor; Bibliometrics; Citation; Value (mathematics); Informetrics; Quality (philosophy); Auteur theory; Computer science; Library science; Positive economics; Statistics; Epistemology; Mathematics; Political science; Economics; History; Philosophy; Law","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","scholarly_communication"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.02236981,0.0001111967,0.000299725,0.0512137,0.000708058,0.00195863,0.003288126,0.00005376024,0.000002283896],"category_scores_gemma":[0.0240512,0.00004455705,0.0003120297,0.4048906,0.001723323,0.002466286,0.0003677083,0.0004015467,0.000003813294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003340954,"about_ca_system_score_gemma":0.0004641948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001379898,"about_ca_topic_score_gemma":6.726358e-7,"domain_scores_codex":[0.9936473,0.0000450412,0.0007883285,0.0001490982,0.004867109,0.0005030982],"domain_scores_gemma":[0.9919136,0.002119211,0.001317451,0.0004055397,0.004112423,0.000131837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003930684,0.00002492261,0.01700092,0.000001151879,0.00001081086,2.455876e-7,0.0003460952,0.00002122633,0.003104166,0.0006798334,0.007370695,0.9714006],"study_design_scores_gemma":[0.002480132,0.005254363,0.8166134,0.00003220499,0.00001477668,0.0002732527,0.01615048,0.02759199,0.0191527,0.02003552,0.09198242,0.0004187614],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874116,0.0007064419,0.0007195485,0.01050185,0.0002517558,0.000275338,0.00000554148,0.000007999336,0.0001199336],"genre_scores_gemma":[0.9983047,0.0005596229,0.0006445586,0.0004454558,0.00001974008,0.000004135604,8.186029e-8,0.000001726323,0.00002001173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9709818,"threshold_uncertainty_score":0.9990774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1618606047685574,"score_gpt":0.5397257732096541,"score_spread":0.3778651684410967,"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."}}