{"id":"W7098619901","doi":"","title":"SCÉES Who gets Market Supplements? Gender Differences within a Large Canadian University","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Seniority; Logistic regression; Payment; Order (exchange); Gender pay gap; Rank (graph theory); Ordered logit; Collective bargaining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002534946,0.000230325,0.0004016541,0.002144097,0.004461263,0.002180978,0.001052477,0.0005707967,0.006603411],"category_scores_gemma":[0.008853036,0.0001915505,0.0003002159,0.002485156,0.001688582,0.0007568103,0.001380048,0.0007097597,0.0006309499],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0179151,"about_ca_system_score_gemma":0.01775094,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8844126,"about_ca_topic_score_gemma":0.954749,"domain_scores_codex":[0.9965013,0.0003043721,0.00007834811,0.0002495955,0.001250959,0.001615307],"domain_scores_gemma":[0.9936954,0.000834148,0.00123062,0.0001711272,0.001528778,0.002539842],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000241354,0.000139116,0.9350218,0.00002502549,0.00002768229,0.000301118,0.02262909,0.0001393303,0.0008837579,0.00260206,0.004174101,0.03381561],"study_design_scores_gemma":[0.000006625593,0.00004362304,0.9537095,0.00002986527,0.000009160433,0.00009336053,0.04006507,0.0003204253,0.0001844749,0.0002625337,0.00525129,0.00002404723],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992897,0.0002229291,0.0001010197,0.001062909,0.00002164777,0.00001277908,0.000155133,0.000005633765,0.005521056],"genre_scores_gemma":[0.9973635,0.0001140202,0.00005841263,0.0001578226,0.000009957737,0.000004363147,0.00006344732,0.000003846801,0.002224676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9974651,"threshold_uncertainty_score":0.2325361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01934971700458522,"score_gpt":0.2426287654522309,"score_spread":0.2232790484476457,"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."}}