{"id":"W2945259118","doi":"10.1007/s11192-019-03120-0","title":"Identifying emerging scholars: seeing through the crystal ball of scholarship selection committees","year":2019,"lang":"en","type":"article","venue":"Scientometrics","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Scholarship; Percentage point; Competition (biology); Psychology; Point (geometry); Selection (genetic algorithm); Medical education; Political science; Medicine; Computer science; Statistics; Mathematics; 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","open_science","insufficient_payload"],"consensus_categories":["metaresearch","bibliometrics","insufficient_payload"],"category_scores_codex":[0.06449754,0.0002801442,0.0005306949,0.09870425,0.000914438,0.006329428,0.005844918,0.0002015694,0.00114377],"category_scores_gemma":[0.08997458,0.0001815673,0.0003771927,0.5707337,0.0003579548,0.003997583,0.001861251,0.001044048,0.0008912222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003159849,"about_ca_system_score_gemma":0.0003203516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001337147,"about_ca_topic_score_gemma":0.00001857007,"domain_scores_codex":[0.9725164,0.0007234549,0.001394401,0.001233613,0.02290142,0.00123073],"domain_scores_gemma":[0.9826747,0.007811437,0.000890065,0.001523926,0.006724983,0.0003748943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002071395,0.0001743237,0.9338319,0.00003397526,0.00004077926,0.000003406964,0.001087983,0.0007888323,0.0168165,0.003786206,0.002913797,0.04050156],"study_design_scores_gemma":[0.001523563,0.0004461774,0.8085988,0.0001040425,0.0000385526,0.00004107736,0.005807728,0.01446434,0.02245904,0.02727951,0.1184528,0.0007843706],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9679592,0.002327723,0.01400093,0.0005679204,0.002319686,0.0005516954,0.0000237063,0.00006038281,0.01218877],"genre_scores_gemma":[0.991471,0.0001721578,0.004858774,0.0001996873,0.00009989599,0.000007697975,0.000002895659,0.00002526965,0.0031626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4720295,"threshold_uncertainty_score":0.9998867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5381788305996781,"score_gpt":0.5673536692852789,"score_spread":0.02917483868560078,"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."}}