{"id":"W3123110294","doi":"10.32920/ryerson.14638719.v1","title":"Empirical Issues in Lifetime Poverty Measurement","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"United Nations University World Institute for Development Economics Research; Department for International Development; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Poverty; Economics; Political science; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09119613,0.001028758,0.002326218,0.003591094,0.004424818,0.00749445,0.005684487,0.004855212,0.007465737],"category_scores_gemma":[0.3647642,0.0009352446,0.00119813,0.01100377,0.01631212,0.0173761,0.006567434,0.01217182,0.0006401059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003525071,"about_ca_system_score_gemma":0.002973254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007662143,"about_ca_topic_score_gemma":0.003344971,"domain_scores_codex":[0.9211457,0.06478737,0.003820749,0.00353467,0.005733767,0.0009777697],"domain_scores_gemma":[0.6863442,0.2745092,0.008087441,0.01411206,0.01558725,0.001359877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00002178514,0.00007031,0.003843421,0.0001532445,0.00004295924,0.0000402525,0.001007659,0.000554542,0.0000348245,0.9666992,0.005039253,0.02249256],"study_design_scores_gemma":[0.00000986309,0.00001269113,0.003304081,0.0002806291,0.0000157463,0.00006650641,0.001271369,0.002395661,0.0001074953,0.9845693,0.007951965,0.00001463798],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.06706408,0.05638074,0.351524,0.4394192,0.00538329,0.0003338677,0.003023351,0.0002238482,0.07664777],"genre_scores_gemma":[0.8309377,0.01456859,0.1142744,0.01540139,0.01148166,0.00178556,0.001371747,0.0002668878,0.009911947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09119613,"threshold_uncertainty_score":0.482297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1233864243348388,"score_gpt":0.3921049880232647,"score_spread":0.2687185636884259,"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."}}