{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00566925,0.0005114507,0.0003429218,0.006441766,0.004480865,0.003167963,0.0005592891,0.001324633,0.06048326],"category_scores_gemma":[0.02298037,0.0002525915,0.0001796693,0.007992848,0.001008655,0.001872062,0.001862838,0.0009475224,0.02961034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002230501,"about_ca_system_score_gemma":0.003797469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01054691,"about_ca_topic_score_gemma":0.01396531,"domain_scores_codex":[0.9941987,0.002018812,0.001100385,0.0004697661,0.001438111,0.0007742043],"domain_scores_gemma":[0.9805854,0.01007599,0.002814124,0.0008109626,0.004482822,0.001230708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002823995,0.0001722887,0.01542748,0.0009562363,0.000008405319,0.0005570716,0.0328549,0.0001301375,0.002282779,0.03943419,0.7853266,0.1225676],"study_design_scores_gemma":[0.00001738793,0.00006308149,0.03381461,0.0007709402,0.000004237812,0.0003056102,0.02788564,0.0001515258,0.001082036,0.002744245,0.9331186,0.00004195071],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1700532,0.008589632,0.005996501,0.04644521,0.006943927,0.001998526,0.1110525,0.001049121,0.6478715],"genre_scores_gemma":[0.5040278,0.00925875,0.01017345,0.01736086,0.001611878,0.005371107,0.07380201,0.0009706285,0.3774235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06048326,"threshold_uncertainty_score":0.2023367,"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."}}