{"id":"W7097460649","doi":"","title":"Gender, Dowry and the Migration System l 357 Gender, Dowry and the Migration System of Indian Information Technology Professionals","year":2016,"lang":"en","type":"article","venue":"","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dowry; Citizenship; Immigration; Information technology; Work (physics); Institution; Population","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":[],"consensus_categories":[],"category_scores_codex":[0.0019341,0.0002057144,0.0001884397,0.001693611,0.01151876,0.007235158,0.0005436111,0.0007840183,0.00399541],"category_scores_gemma":[0.003265454,0.0001535357,0.0002061571,0.002123903,0.0119327,0.002624396,0.006003467,0.001367229,0.0002584385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00365768,"about_ca_system_score_gemma":0.004841725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04297239,"about_ca_topic_score_gemma":0.04499491,"domain_scores_codex":[0.997788,0.001020412,0.00006986638,0.0001188204,0.0002321116,0.0007709391],"domain_scores_gemma":[0.9983058,0.0003938702,0.0004181069,0.00007089788,0.0002082883,0.0006030301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00006567804,0.0001107448,0.09603488,0.0001028539,0.00001395901,0.000794476,0.7426729,0.00008257206,0.0005355514,0.1165613,0.004200696,0.03882437],"study_design_scores_gemma":[0.000008503574,0.00007505669,0.07234753,0.0002465833,0.00001043007,0.0004555834,0.8736724,0.000116046,0.0001254751,0.005826565,0.04708175,0.00003408508],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.914788,0.0019219,0.0003618453,0.007872446,0.0001430623,0.00002022652,0.00003190949,0.000009690429,0.07485085],"genre_scores_gemma":[0.9964433,0.0005352477,0.00006900357,0.0003298525,0.00001827628,0.000005278096,0.000007909392,0.000002578727,0.002588537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04297239,"threshold_uncertainty_score":0.08544451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01085889250334054,"score_gpt":0.2573121985757864,"score_spread":0.2464533060724459,"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."}}