{"id":"W2010664765","doi":"10.1145/820127.820188","title":"Gender and information technology","year":2002,"lang":"en","type":"article","venue":"ACM SIGCSE Bulletin","topic":"Gender and Technology in Education","field":"Social Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Discipline; Variety (cybernetics); Government (linguistics); Information technology; Public relations; Matching (statistics); Sociology; Convergence (economics); Engineering ethics; Political science; Social science; Engineering; Law; Economic growth; Computer science; Economics; Medicine","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":["insufficient_payload"],"category_scores_codex":[0.0001690689,0.0000529213,0.00005627483,0.0001573534,0.0002819835,0.00003057764,0.0002190975,0.0001809093,0.001617273],"category_scores_gemma":[0.0007721807,0.00005464883,0.00001184279,0.0002161338,0.0002231705,0.0001271582,0.00006640433,0.0001139726,0.001077228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002542823,"about_ca_system_score_gemma":0.00001401304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007294024,"about_ca_topic_score_gemma":0.000008919863,"domain_scores_codex":[0.9994994,0.00002676632,0.0001013914,0.00009006225,0.0001030759,0.0001792938],"domain_scores_gemma":[0.9996179,0.00004754437,0.0000415431,0.0002075343,0.00004835631,0.00003715414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004029929,0.0001114994,0.02320473,0.00001803177,0.00002952117,0.000002945373,0.03942025,0.000001356169,0.00008762889,0.4318586,0.2228634,0.282398],"study_design_scores_gemma":[0.0001210068,0.00001384166,0.002436018,0.000001862437,0.000005787635,0.000004801392,0.01078936,0.000003392005,0.0001165558,0.0199133,0.9665149,0.00007918079],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4364668,0.00100571,0.0003206783,0.2098311,0.000600433,0.000398785,0.000002626208,0.0007888401,0.350585],"genre_scores_gemma":[0.9959584,0.0003692941,0.002143386,0.0006797089,0.00004852959,0.00002205113,0.000001579814,0.000003013367,0.0007739793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7436515,"threshold_uncertainty_score":0.9997005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0257160157195255,"score_gpt":0.2687064808119452,"score_spread":0.2429904650924197,"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."}}