{"id":"W7099938275","doi":"","title":"GOVERNMENT MACHINERIES AND PROGRAMMES RELEVANT TO WOMEN","year":2016,"lang":"en","type":"article","venue":"","topic":"Microfinance and Financial Inclusion","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agency (philosophy); Government (linguistics); Advice (programming); Wish","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002442561,0.000100621,0.0002035967,0.00003799317,0.00008017777,0.00003301759,0.00009473653,0.00004510221,0.0004714422],"category_scores_gemma":[0.00007298338,0.00007296716,0.00002641975,0.00008562361,0.00003603836,0.0001306186,0.0001718417,0.00002924197,0.0007018397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001446093,"about_ca_system_score_gemma":0.00000506353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008236,"about_ca_topic_score_gemma":0.00003045497,"domain_scores_codex":[0.9991262,0.000002855205,0.0002693301,0.0002921562,0.0000300437,0.0002794347],"domain_scores_gemma":[0.9996308,0.00001680384,0.0000749935,0.0001786568,0.000007926018,0.00009080087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007274828,0.00008305795,0.0734474,0.00001940344,0.00001538105,0.000004775936,0.001645374,1.99604e-7,0.003487395,0.7007318,0.007897809,0.2125946],"study_design_scores_gemma":[0.0003715977,0.0002126301,0.03258604,0.00001816092,6.8974e-7,0.000002478916,0.0000990803,0.000006291493,0.001170918,0.03520549,0.9300995,0.0002270646],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9606966,0.0006610308,0.009686501,0.004733701,0.000171671,0.0002242089,0.00009778608,0.00004527523,0.02368321],"genre_scores_gemma":[0.9780464,0.0005873029,0.001861831,0.0007736773,0.00005975588,0.00006492635,5.366293e-7,0.00001386151,0.01859169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9222018,"threshold_uncertainty_score":0.9020965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01168380046451101,"score_gpt":0.1973910687919956,"score_spread":0.1857072683274845,"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."}}