{"id":"W4409415928","doi":"10.1073/pnas.2400700121","title":"Toward multiscalar measures of inequality in archaeology","year":2025,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Historical Economic and Social Studies","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Arthur B. McDonald-Canadian Astroparticle Physics Research Institute","funders":"Santa Fe Institute; National Science Foundation","keywords":"Gini coefficient; Inequality; Polity; Proxy (statistics); Subsistence agriculture; Human settlement; Socioeconomic status; Geography; Econometrics; Scale (ratio); Economic geography; Economic inequality; Sociology; Archaeology; Economics; Statistics; Mathematics; Demography; Agriculture; Political science; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001530477,0.00004452121,0.0002636812,0.0001815321,0.0000527029,0.000002379322,0.000403967,0.00006351116,0.000008473553],"category_scores_gemma":[0.001136311,0.00003747065,0.00006465999,0.0004756225,0.0008430895,0.0001051323,0.0001140701,0.00008667786,0.000001016918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007311767,"about_ca_system_score_gemma":0.00001484716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001921352,"about_ca_topic_score_gemma":0.000001529494,"domain_scores_codex":[0.9991657,0.000003331273,0.0005152648,0.0001497797,0.00007856682,0.00008737598],"domain_scores_gemma":[0.9994208,0.000095351,0.0004025213,0.00000477092,0.00006689616,0.000009636558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004685717,0.00002625305,0.2374771,0.0000325796,0.00001033057,6.556248e-10,0.000811213,0.00002456139,0.0003425795,0.7609155,0.0001561652,0.0001990872],"study_design_scores_gemma":[0.0001386485,0.0000116574,0.4177389,0.00002123522,0.000001417332,7.618335e-8,0.0001978119,0.0002303763,0.002214661,0.5777228,0.001683831,0.00003865247],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9448227,0.001395641,0.00001845766,0.006173712,0.00004532764,0.0001038566,0.0000210518,0.000003154035,0.0474161],"genre_scores_gemma":[0.9992449,0.00008938986,0.0003496362,0.0001430212,0.00001353046,0.000005532117,2.158521e-8,9.577356e-7,0.0001529537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1831927,"threshold_uncertainty_score":0.3106399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1467434845081259,"score_gpt":0.3088111365502891,"score_spread":0.1620676520421632,"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."}}