{"id":"W2401109030","doi":"10.3968/8371","title":"Sexual Discrimination in Italian and Chinese","year":2016,"lang":"en","type":"article","venue":"Studies in literature and language","topic":"Linguistic Studies and Language Acquisition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Markedness; Phenomenon; Linguistics; Sexual discrimination; Semantics (computer science); Word order; Chinese language; Psychology; Order (exchange); Sociology; History; Gender studies; Philosophy; Epistemology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008154825,0.0002275275,0.0002434848,0.001389836,0.001995806,0.001117294,0.0002894974,0.000356109,0.005431622],"category_scores_gemma":[0.002675646,0.0001066622,0.0002241146,0.002621245,0.001521425,0.000581717,0.001053525,0.0005940189,0.0003628328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001809338,"about_ca_system_score_gemma":0.001909944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1349476,"about_ca_topic_score_gemma":0.1685975,"domain_scores_codex":[0.999258,0.0001058908,0.00003755381,0.00009442592,0.0001836276,0.0003204866],"domain_scores_gemma":[0.9992099,0.000122543,0.0002764738,0.00005636448,0.00009924762,0.0002354998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.000324616,0.0003278471,0.6939247,0.0001642539,0.00004885807,0.005639243,0.2209701,0.0000573195,0.001949574,0.01165933,0.004417039,0.0605173],"study_design_scores_gemma":[0.0000133481,0.00008623496,0.9456149,0.00006488791,0.00002437928,0.001402026,0.03918608,0.0001101661,0.000247587,0.0005476244,0.01268082,0.00002196481],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868754,0.0003844294,0.00001993334,0.0004817103,0.00001780015,0.00001175211,0.00005688672,0.000002052989,0.01215001],"genre_scores_gemma":[0.9964815,0.0004315503,0.00002424208,0.0001952479,0.00001548516,0.000009550518,0.00006904308,0.000002932944,0.002770451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1349476,"threshold_uncertainty_score":0.2683244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009906478999100786,"score_gpt":0.3002538966199681,"score_spread":0.2903474176208673,"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."}}