{"id":"W27731849","doi":"10.2196/mededu.4958","title":"TYPE AND FUNCTION OF CODE MIXING IN WOMEN LANGUAGE: AN ANALYSIS IN GOOD HOUSEKEEPING\\'S MAGAZINE","year":2013,"lang":"en","type":"dissertation","venue":"JMIR Medical Education","topic":"Media, Gender, and Advertising","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Housekeeping; Mixing (physics); Function (biology); Code-mixing; Code (set theory); Computer science; Linguistics; Programming language; Biology; Physics; Philosophy; Code-switching; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008373472,0.0001089166,0.0002948859,0.0005551718,0.00005753361,0.00003119565,0.000127978,0.0003424257,0.0007621167],"category_scores_gemma":[0.0009644073,0.0001143594,0.00002787872,0.00119899,0.0000630475,0.0001933667,0.000008022991,0.0002711189,0.000008015203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002299779,"about_ca_system_score_gemma":0.001647642,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00506817,"about_ca_topic_score_gemma":0.03763931,"domain_scores_codex":[0.998367,0.0002296497,0.000355895,0.0002670161,0.0005490026,0.0002315063],"domain_scores_gemma":[0.999254,0.00007282764,0.0001915288,0.0001292361,0.0001228449,0.0002295929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001135501,0.0007008936,0.03752833,0.000372804,0.00007670893,0.000002926675,0.5518808,0.000007517061,0.0006064413,0.0007985855,0.0005413389,0.4073701],"study_design_scores_gemma":[0.0003521444,0.00006633234,0.7260337,0.0003799548,0.00008367086,2.285765e-7,0.2703942,0.0003171677,0.00002755622,0.0004789958,0.001652017,0.0002140035],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937165,0.0006813382,0.000009826646,0.0002128321,0.0007381362,0.0002751707,0.000001249748,0.00001909668,0.004345833],"genre_scores_gemma":[0.9956309,0.0003967919,0.00005838095,0.0001833002,0.0003197175,0.00008686069,0.0004019066,0.00001378798,0.002908357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6885054,"threshold_uncertainty_score":0.9799213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01230562822786405,"score_gpt":0.3598664205086876,"score_spread":0.3475607922808235,"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."}}