{"id":"W2208965862","doi":"","title":"Normative Choices and Tradeoffs when Measuring Poverty over Time","year":2012,"lang":"en","type":"preprint","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Bundesministerium für Wirtschaftliche Zusammenarbeit und Entwicklung; Australian Agency for International Development; Georg-August-Universität Göttingen; University of Oxford; International Development Research Centre; Economic and Social Research Council; International Fine Particle Research Institute; United Nations Development Programme; Robertson Foundation; UNICEF","keywords":"Normative; Poverty; Measure (data warehouse); Context (archaeology); Economics; Positive economics; Measuring poverty; Public economics; Culture of poverty; Basic needs; Chronic poverty; Econometrics; Computer science; Political science; Poverty reduction; Economic growth; Geography; Law","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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.003836066,0.0004943914,0.0009428008,0.001167037,0.003136213,0.0001527077,0.002604146,0.0006354438,0.001509313],"category_scores_gemma":[0.0003208485,0.0006266441,0.0004785564,0.0006550368,0.003210198,0.001868636,0.004487674,0.002290536,0.00003788805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071992,"about_ca_system_score_gemma":0.001256097,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02854166,"about_ca_topic_score_gemma":0.01332667,"domain_scores_codex":[0.9922576,0.002753337,0.0003095575,0.000971624,0.00213752,0.001570362],"domain_scores_gemma":[0.9959486,0.001045075,0.0005107679,0.0008244163,0.0007008243,0.0009703677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005465318,0.002245279,0.2633336,0.003128313,0.002837822,0.0004757096,0.4562108,0.0001049121,0.0009154578,0.1676026,0.06843944,0.02924071],"study_design_scores_gemma":[0.00207705,0.0002534977,0.0798678,0.0004747645,0.0002327201,0.000003252804,0.04322903,0.001082846,0.0000268898,0.0207289,0.8509009,0.001122328],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3869439,0.0003690067,0.00278767,0.002515305,0.0004151058,0.001837778,0.00190283,0.0002447695,0.6029837],"genre_scores_gemma":[0.9332079,0.01202878,0.007798908,0.0001791791,0.0007167368,0.000001253362,0.0004929811,0.00009397887,0.0454803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7824615,"threshold_uncertainty_score":0.9996185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06581439754608004,"score_gpt":0.2935022786111801,"score_spread":0.2276878810651001,"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."}}