{"id":"W2082264049","doi":"10.1007/s11024-011-9170-6","title":"Equity and Excellence in Research Funding","year":2011,"lang":"en","type":"article","venue":"Minerva","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":79,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Excellence; Equity (law); Incentive; Inequality; Inequity aversion; Normality; Economics; Public economics; Distribution (mathematics); Higher education; Positive economics; Actuarial science; Political science; Microeconomics; Psychology; Social psychology; Law; Economic growth","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"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.06383326,0.00007240383,0.0001646629,0.04339758,0.0001612823,0.000895174,0.001846994,0.00007531619,0.0009035632],"category_scores_gemma":[0.03320067,0.00005034673,0.00003176417,0.1312568,0.0002784922,0.0004404502,0.003387849,0.0003256291,0.0003947139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007322059,"about_ca_system_score_gemma":0.0000865471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001302729,"about_ca_topic_score_gemma":0.0002419294,"domain_scores_codex":[0.9913238,0.0003196562,0.0003893225,0.0005908293,0.006572747,0.0008035995],"domain_scores_gemma":[0.9947398,0.003207448,0.0000518951,0.0005289532,0.001106157,0.0003657759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001906703,0.0001065948,0.60331,0.000005168415,0.000001781223,0.00005903017,0.0008104398,6.005741e-7,0.002433019,0.002560382,0.007274997,0.3834189],"study_design_scores_gemma":[0.0003266391,0.0001725881,0.9141244,0.00001715551,7.514756e-7,0.000009629259,0.001110145,0.003042243,0.00133162,0.06609846,0.01362487,0.0001415337],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8606405,0.001139831,0.0002424324,0.0004875566,0.00015754,0.0001143421,0.000002611148,0.000008071489,0.1372072],"genre_scores_gemma":[0.9934939,0.0001770764,0.0009589246,0.00004191459,0.00003294301,0.000007557613,1.903407e-7,0.000004643536,0.005282881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3832773,"threshold_uncertainty_score":0.9893383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9765732857704374,"score_gpt":0.7217798884682964,"score_spread":0.254793397302141,"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."}}