{"id":"W4393411397","doi":"10.18260/1-2--45482","title":"Tailoring DEIA Programming through Current Field Analysis: Promoting Allyship in STEM of University Graduate Students","year":2024,"lang":"en","type":"article","venue":"","topic":"Engineering Education and Curriculum Development","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Illinois at Urbana-Champaign; U.S. Army Corps of Engineers; Clemson University; Calvin University; American Society for Engineering Education","keywords":"Computer science; Field (mathematics); Current (fluid); Software engineering; Engineering; Mathematics; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002314549,0.0001098875,0.0001532822,0.0002495028,0.00001626922,0.00004104124,0.0001526111,0.00003128831,0.00002066683],"category_scores_gemma":[0.000007948501,0.0001108763,0.00006843849,0.001194069,0.000005109752,0.0001398039,0.00003819322,0.0001742948,0.000007584615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000124365,"about_ca_system_score_gemma":0.00002169013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003373745,"about_ca_topic_score_gemma":0.00005136714,"domain_scores_codex":[0.9992396,0.00001694286,0.0002098017,0.0001569862,0.0001952388,0.0001814945],"domain_scores_gemma":[0.9997768,0.00004326436,0.00001443206,0.0001026857,0.00002276733,0.00004009927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001781203,0.0006527805,0.427597,0.006393242,0.003550459,0.0001107998,0.05668203,0.1308485,0.0007963429,0.008134015,0.001293652,0.3639233],"study_design_scores_gemma":[0.001714297,0.0002088117,0.4139886,0.005612647,0.001700165,0.00001353412,0.02332683,0.4441637,0.03438156,0.0002449318,0.07151899,0.003125902],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760607,0.0006067347,0.02152803,0.00004989543,0.0005337797,0.0001641555,8.29354e-7,0.0003834477,0.0006723666],"genre_scores_gemma":[0.9977187,0.0001000242,0.002010911,0.000002624975,0.00001749589,0.000006961784,0.000004227127,0.0000114564,0.0001276067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3607974,"threshold_uncertainty_score":0.4521403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0325975374798157,"score_gpt":0.2824615649332987,"score_spread":0.249864027453483,"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."}}