{"id":"W4388841092","doi":"10.22617/tim230491-2","title":"Using Administrative Data to Strengthen Development Statistics in Asia and the Pacific","year":2023,"lang":"en","type":"report","venue":"","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Employment and Social Development Canada; International Labour Organization; Info-communications Media Development Authority","keywords":"Government (linguistics); Work (physics); Social statistics; Data quality; Social security; Sustainable development; Official statistics; Business; Quality (philosophy); Political science; Computer science; Statistics; Marketing; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01175048,0.0002147098,0.0004743274,0.0004563553,0.0001648606,0.0004723998,0.0008953131,0.0001286771,0.00008161622],"category_scores_gemma":[0.006256077,0.0001208022,0.00002709863,0.0008910152,0.000124792,0.000110473,0.0005600531,0.0002433497,0.0000694929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000646024,"about_ca_system_score_gemma":0.001630171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000636759,"about_ca_topic_score_gemma":0.007162763,"domain_scores_codex":[0.9950144,0.000250106,0.001202381,0.0008327273,0.002461379,0.0002389969],"domain_scores_gemma":[0.9952283,0.002716942,0.0003777437,0.001044521,0.0005328949,0.00009958667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002642794,0.0001215499,0.008279133,0.00009880614,0.0002900377,0.0001622392,0.01466178,0.003385818,0.00000452448,0.006490237,0.2221864,0.7440552],"study_design_scores_gemma":[0.001606783,0.00009992167,0.02742586,0.0006256378,0.0001827909,0.0000750414,0.08402775,0.4672628,0.000004803688,0.02507733,0.3922341,0.00137718],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1159227,0.001553097,0.531087,0.004598918,0.007321073,0.005811938,0.008578704,0.0004289146,0.3246976],"genre_scores_gemma":[0.6552199,0.003589456,0.2712991,0.0001914227,0.0003387998,0.0001272654,0.002340135,0.0001488813,0.06674509],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.742678,"threshold_uncertainty_score":0.7489561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.791630166184077,"score_gpt":0.5380147060040701,"score_spread":0.2536154601800069,"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."}}