{"id":"W3088246073","doi":"10.1177/0964663920960536","title":"The Travels of a Set of Numbers: The Multiple Networks Enabled by the Colombian ‘Estrato’ System","year":2020,"lang":"en","type":"article","venue":"Social & Legal Studies","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Solidarity; Subsidy; Zoning; Payment; Core (optical fiber); Class (philosophy); Work (physics); Differential (mechanical device); Sociology; Law; Economics; Mathematical economics; Computer science; Political science; Telecommunications; Politics; Artificial intelligence; Finance","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001770751,0.0002568019,0.0003060125,0.001151691,0.008532777,0.007019133,0.0006544018,0.0008253979,0.007438981],"category_scores_gemma":[0.005536396,0.0002495866,0.0001497541,0.001074042,0.0178642,0.005950818,0.004664388,0.001854995,0.0004006344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00876265,"about_ca_system_score_gemma":0.003056224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09373095,"about_ca_topic_score_gemma":0.1609746,"domain_scores_codex":[0.9967201,0.002142713,0.00006987112,0.0003498064,0.000369821,0.0003476874],"domain_scores_gemma":[0.9979942,0.0006810474,0.0002822844,0.0004499957,0.0003054485,0.0002871126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003397505,0.00001074734,0.003162886,0.00007414919,0.000005769589,0.0002187103,0.05952898,0.0004866854,0.0002664014,0.9058028,0.006659161,0.02374967],"study_design_scores_gemma":[0.00003341307,0.00004999811,0.01390402,0.0003849417,0.00002470516,0.0004493929,0.1421497,0.002224252,0.0004044727,0.1835068,0.6567755,0.00009284356],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2011935,0.002201888,0.02154079,0.02485722,0.0001911377,0.00006703943,0.0002803376,0.0001512073,0.749517],"genre_scores_gemma":[0.9848251,0.0003431893,0.003229944,0.0003831121,0.00002301191,0.00004206293,0.00004058948,0.00003277821,0.01108029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9914672,"threshold_uncertainty_score":0.1863708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0629452444099069,"score_gpt":0.3327908815253552,"score_spread":0.2698456371154483,"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."}}