{"id":"W4414567775","doi":"10.31235/osf.io/bsmzy_v1","title":"Unregulated, Unknown and Highly Necessary: Grey Market Nannies","year":2025,"lang":"en","type":"article","venue":"","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Devaluation; Inequality; Work (physics); Wage inequality; Wage; Public policy; Care work; Certification; Private sector","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":[0.0006303155,0.0001524167,0.0002956213,0.001179036,0.003318685,0.002581506,0.0006030808,0.0003126763,0.008743689],"category_scores_gemma":[0.002175555,0.000111562,0.0001749487,0.001864015,0.001684198,0.00133626,0.001989768,0.0004684118,0.0005355117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009338806,"about_ca_system_score_gemma":0.006319168,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8348683,"about_ca_topic_score_gemma":0.9455842,"domain_scores_codex":[0.9994162,0.00006856293,0.00001102007,0.00008523955,0.0001830302,0.0002360708],"domain_scores_gemma":[0.9991205,0.0001762134,0.0001661581,0.000086217,0.0002329547,0.0002179129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004813751,0.0001123785,0.6366523,0.0002158831,0.00007289153,0.001789213,0.128286,0.00129711,0.004922722,0.04566627,0.0607187,0.1197853],"study_design_scores_gemma":[0.00001312137,0.00004409157,0.6364986,0.0001572506,0.00002285877,0.000307993,0.2102882,0.006190531,0.001072143,0.004532927,0.1408169,0.00005530026],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9696017,0.0001879249,0.001109647,0.002172121,0.00003190231,0.00004138375,0.001854044,0.00004154469,0.02495964],"genre_scores_gemma":[0.9908626,0.0001108512,0.0005613725,0.0002921258,0.000008909024,0.00001060304,0.0005884505,0.00002952325,0.007535734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8348683,"threshold_uncertainty_score":0.3322083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007316271148434142,"score_gpt":0.2473053939039876,"score_spread":0.2399891227555535,"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."}}