{"id":"W3177009914","doi":"10.1017/qrd.2021.3","title":"A large ‘discovery’ experiment: Gender Initiative for Excellence (Genie) at Chalmers University of Technology","year":2021,"lang":"en","type":"editorial","venue":"QRB Discovery","topic":"Interdisciplinary Research and Collaboration","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fondation Chalmers","keywords":"Excellence; Euros; Gender equality; Inequality; Political science; Gender inequality; Sociology; Management; Operational excellence; Library science; Public relations; Engineering; Gender studies; Economics; Computer science; Operations management; Humanities; Law; Art","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001334453,0.0003724602,0.0008206366,0.0006751323,0.0005184496,0.0003313176,0.001494491,0.0008198514,0.0004513566],"category_scores_gemma":[0.002516568,0.0003418171,0.0004571708,0.001352782,0.0004159336,0.001494915,0.001837429,0.0004830428,0.00009215479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005780322,"about_ca_system_score_gemma":0.002343996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001907402,"about_ca_topic_score_gemma":0.0004646671,"domain_scores_codex":[0.9947251,0.0003096017,0.0006438906,0.001239184,0.002424892,0.0006573002],"domain_scores_gemma":[0.9944118,0.001896176,0.0006866661,0.001063489,0.001825304,0.0001166127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009816618,0.0002056602,0.00006173431,0.0000682104,0.0001498214,0.00007462175,0.002492717,0.00001548574,0.001390782,0.001714408,0.9927042,0.0001406871],"study_design_scores_gemma":[0.001680755,0.0006461749,0.00002980316,0.0001545764,0.00006402365,0.000002239652,0.05642253,0.0001384857,0.007696646,0.006636816,0.9260078,0.000520141],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.04114486,0.006833854,0.0560399,0.001687176,0.8483652,0.002709728,0.02016404,0.0001129619,0.02294225],"genre_scores_gemma":[0.3961116,0.001234375,0.0009514636,0.00007273698,0.3367277,0.0001662168,0.003626989,0.0001650693,0.2609439],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.5116375,"threshold_uncertainty_score":0.9999034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06791950712618094,"score_gpt":0.3858810995504988,"score_spread":0.3179615924243179,"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."}}