{"id":"W4391358919","doi":"10.29173/spectrum222","title":"Bridging the Gender Gap: A Canadian Study Examining Gender Inequality in Engineering Workplaces","year":2024,"lang":"en","type":"article","venue":"Spectrum","topic":"Career Development and Diversity","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Bridging (networking); Inequality; Gender inequality; Gender gap; Gender equality; Sociology; Political science; Psychology; Gender studies; Demographic economics; Computer science; Mathematics; Economics; Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001382172,0.00008634404,0.00008852232,0.0001857092,0.0003140961,0.0002496525,0.0002090904,0.00003991844,0.0002660173],"category_scores_gemma":[0.00007072547,0.00007552558,0.00002676733,0.0006222524,0.00003309789,0.0001561318,0.00006335125,0.0001974849,0.00004545252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004760683,"about_ca_system_score_gemma":0.0003793447,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2703942,"about_ca_topic_score_gemma":0.7467985,"domain_scores_codex":[0.9988835,0.00009037221,0.0001114029,0.0002132539,0.0002887034,0.00041271],"domain_scores_gemma":[0.9996696,0.00009208819,0.00001178675,0.0001061595,0.000009737224,0.0001106762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002651453,0.00001275847,0.8365151,0.00001377321,0.00003836492,0.0001625337,0.1540453,0.000102442,0.000004015314,0.007053668,0.001040462,0.001008892],"study_design_scores_gemma":[0.0001446976,0.000009498835,0.9252856,0.00002684379,0.00001720971,0.000001293837,0.06549557,0.0006063592,0.000008073687,0.000800397,0.007367821,0.0002366122],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668116,0.0003131763,0.0001068162,0.001260511,0.0007608052,0.0002387555,0.000003157098,0.0001078191,0.03039737],"genre_scores_gemma":[0.9991968,0.00001075684,0.00002164861,0.00008305384,0.0002491928,0.000006402757,0.000001490534,0.000007218567,0.0004233844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4764043,"threshold_uncertainty_score":0.7344643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08833135761039616,"score_gpt":0.291795469539935,"score_spread":0.2034641119295388,"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."}}