{"id":"W4386024226","doi":"10.3386/w31571","title":"How to Fund Unemployment Insurance with Informality and False Claims: Evidence From Senegal","year":2023,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Unemployment; Economics; Business; Actuarial science; Economic growth","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.004655161,0.0004768355,0.0003786016,0.001229626,0.0015514,0.002046331,0.001089863,0.001008111,0.003707765],"category_scores_gemma":[0.01584759,0.0003073941,0.0007332716,0.001475578,0.00202818,0.001477782,0.003390803,0.001283992,0.0004022687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003033186,"about_ca_system_score_gemma":0.00330778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1211226,"about_ca_topic_score_gemma":0.1865921,"domain_scores_codex":[0.9955408,0.00273056,0.0001219138,0.0001734785,0.0005253621,0.0009078788],"domain_scores_gemma":[0.9855106,0.005296147,0.005941278,0.001009235,0.001163362,0.001079384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002282679,0.001719381,0.8542014,0.0007121745,0.001103401,0.001314653,0.004554497,0.004824617,0.0009193983,0.01198996,0.008548964,0.1078289],"study_design_scores_gemma":[0.0006343679,0.001689151,0.9419188,0.00104747,0.001029849,0.0006576032,0.02106766,0.003870213,0.001100305,0.003604019,0.02331418,0.00006636086],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861892,0.001560713,0.0002871722,0.003023097,0.00002059419,0.00004622377,0.0006204369,0.00001368136,0.008238869],"genre_scores_gemma":[0.997622,0.0008521669,0.000116071,0.0003203909,0.00001051206,0.00002332398,0.0001662956,0.000002830073,0.0008865682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1211226,"threshold_uncertainty_score":0.2408352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6983622558909468,"score_gpt":0.6190768136905873,"score_spread":0.07928544220035949,"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."}}