{"id":"W2154132674","doi":"","title":"Inspiring Girls and their Female After School Educators to Pursue Computer Science and other STEM Careers","year":2012,"lang":"en","type":"article","venue":"International Journal of Gender, Science, and Technology","topic":"Career Development and Diversity","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workforce; Women in science; Science education; Medical education; Psychology; Mathematics education; Political science; Sociology; Medicine; Gender studies","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":[0.001925305,0.00008321693,0.0001144721,0.001135942,0.0004402873,0.0002470864,0.0006132019,0.00005041222,0.00001952916],"category_scores_gemma":[0.000180213,0.00006757519,0.00001575663,0.000776118,0.00210072,0.0008545693,0.0004261946,0.0001263588,0.000003535758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00025748,"about_ca_system_score_gemma":0.0007390632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008248162,"about_ca_topic_score_gemma":0.00007027193,"domain_scores_codex":[0.9985215,0.00001560201,0.0001557717,0.0001900212,0.0008022492,0.0003148525],"domain_scores_gemma":[0.9985363,0.00002936809,0.00009745644,0.00006777401,0.0009332664,0.0003358352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001387967,0.0000242043,0.9401159,0.000002269648,0.00001752458,0.000005096763,0.01448559,4.553064e-7,0.000716213,0.007430073,0.0002151611,0.03697368],"study_design_scores_gemma":[0.001110299,0.0002040853,0.7423285,0.0001118035,0.00002965584,0.0004533615,0.09839967,0.00005509257,0.006633415,0.002890748,0.1471735,0.0006098305],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942758,0.0004625866,0.0002155906,0.002123216,0.001300564,0.00007603447,0.00000292069,0.00001576574,0.001527542],"genre_scores_gemma":[0.9980113,0.0000615172,0.00106004,0.0005914225,0.0002200807,0.000001614436,3.408205e-8,0.000002826397,0.00005114531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1977873,"threshold_uncertainty_score":0.7740189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02693169698020324,"score_gpt":0.2887791396843251,"score_spread":0.2618474427041219,"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."}}