{"id":"W4390315366","doi":"10.1145/3623762.3633497","title":"Modeling Women's Elective Choices in Computing","year":2023,"lang":"en","type":"article","venue":"","topic":"Gender and Technology in Education","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Curriculum; Appeal; Class (philosophy); Institution; Medical education; Mathematics education; Psychology; Medicine; Artificial intelligence; Pedagogy; Sociology; Social science","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":[],"consensus_categories":[],"category_scores_codex":[0.0004469173,0.00002611373,0.00004033351,0.0001323048,0.0001715977,0.00001563421,0.00009039352,0.00005010277,0.00004264346],"category_scores_gemma":[0.00009707369,0.00002682132,0.00000769591,0.0006928186,0.00002940968,0.00006135619,0.00001760658,0.00007116461,0.00007183501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001040971,"about_ca_system_score_gemma":0.00006417665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002068837,"about_ca_topic_score_gemma":0.002430179,"domain_scores_codex":[0.9995118,0.00003090844,0.00006058734,0.00009464967,0.00007579391,0.0002262203],"domain_scores_gemma":[0.9998683,0.00004200724,0.000008695075,0.00004062484,0.00002081449,0.00001956232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000004339711,0.0001139743,0.1646716,0.00001051678,0.00002191085,0.000003775335,0.5512276,0.02005001,0.0002850906,0.2047797,0.0005880308,0.0582435],"study_design_scores_gemma":[0.0002051142,0.00002052678,0.02451346,0.000009709201,0.000001882498,2.632886e-7,0.652298,0.1696435,0.0001183013,0.1512074,0.001794441,0.0001874571],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.946939,0.000006769607,0.0005524727,0.001058268,0.0001560532,0.00005453773,5.68192e-8,0.0002712634,0.0509616],"genre_scores_gemma":[0.998816,0.00001359517,0.0001529433,0.00006232678,0.00005452695,0.000008711208,6.082849e-7,0.000002220914,0.0008890523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1495935,"threshold_uncertainty_score":0.3127477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03759702097350767,"score_gpt":0.3598807212824586,"score_spread":0.322283700308951,"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."}}