{"id":"W2745143180","doi":"10.18260/1-2--28401","title":"Gendered Words in U.S. Engineering Recruitment Documents","year":2018,"lang":"en","type":"article","venue":"","topic":"Gender Studies in Language","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"American Society for Engineering Education","keywords":"Computer science; Natural language processing","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003817678,0.00006203062,0.00007631967,0.00005560146,0.000119462,0.00002674483,0.0001653338,0.00003857466,0.00120413],"category_scores_gemma":[0.0001044111,0.00005902573,0.00001934483,0.0002382087,0.00008132805,0.0000784261,0.00008049545,0.00005023857,0.0001478672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001851032,"about_ca_system_score_gemma":0.00003065487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001580015,"about_ca_topic_score_gemma":0.0058988,"domain_scores_codex":[0.9991847,0.0000367422,0.0001082294,0.0001458808,0.0002105777,0.0003138662],"domain_scores_gemma":[0.9997565,0.00002764923,0.00001601361,0.0001244076,0.00002182491,0.00005367251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002442912,0.0002285898,0.07360428,0.00003466044,0.0001520531,0.00006196097,0.7164867,0.00006473634,0.000717541,0.1100191,0.02440004,0.07420594],"study_design_scores_gemma":[0.001250766,0.000128332,0.03923627,0.00005275323,0.00001456469,0.000001238126,0.1058771,0.0003959116,0.001504982,0.002474267,0.8483702,0.0006936635],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.136226,0.0002488069,0.001526959,0.0011094,0.001470192,0.0005539883,8.334575e-7,0.0001760531,0.8586878],"genre_scores_gemma":[0.9912558,0.00004228723,0.003195397,0.0001592257,0.0002562452,0.00004149807,3.933695e-7,0.00000597354,0.00504316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8550298,"threshold_uncertainty_score":0.9997089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07289939630267257,"score_gpt":0.366851850710825,"score_spread":0.2939524544081524,"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."}}