{"id":"W2107222004","doi":"10.1002/asi.21621","title":"What does the g‐index really measure?","year":2011,"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":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Measure (data warehouse); Index (typography); Consistency (knowledge bases); Statistics; Mathematics; Internal consistency; Econometrics; Computer science; Data mining; Discrete mathematics; Psychometrics","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","sts","scholarly_communication"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.0297371,0.00007989994,0.0002115356,0.008200603,0.0008355225,0.001680308,0.004269239,0.00005238709,0.000009037858],"category_scores_gemma":[0.01865572,0.00002902411,0.0002393418,0.1029407,0.004324087,0.005479377,0.0006360595,0.0002998142,0.000006321642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009393606,"about_ca_system_score_gemma":0.0005943317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001711785,"about_ca_topic_score_gemma":0.000004363906,"domain_scores_codex":[0.9936627,0.00003367093,0.000676581,0.000138832,0.005140484,0.0003477765],"domain_scores_gemma":[0.9877393,0.000736964,0.001584049,0.0005327801,0.009289837,0.0001170837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003482686,0.00002673919,0.02061602,0.000002534618,0.00002902795,2.003985e-7,0.003068873,0.000003944443,0.0007593007,0.007444259,0.01234639,0.9556679],"study_design_scores_gemma":[0.001235086,0.001223541,0.1737075,0.00004417023,0.00004727094,0.0003436687,0.2522702,0.007261829,0.01022197,0.1314489,0.4218263,0.0003695844],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9438462,0.0003415873,0.01151299,0.0407843,0.001593994,0.000407451,0.00000788359,0.00001993976,0.001485698],"genre_scores_gemma":[0.9954975,0.0007664773,0.001958956,0.001652788,0.00003031992,0.000005173761,4.113875e-8,0.000002110586,0.00008666925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9552983,"threshold_uncertainty_score":0.999356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2563697079422282,"score_gpt":0.4736868469626427,"score_spread":0.2173171390204145,"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."}}