{"id":"W6950572767","doi":"10.5683/sp3/nklvfp","title":"Replication Data for: How to Model IWS: Comparing Modelling Choices & Their Impact on Inequality","year":2023,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Replication (statistics); Path (computing); Code (set theory); Inequality; Data modeling; Measure (data warehouse)","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":[0.003487526,0.001536579,0.001104344,0.001454338,0.001044391,0.00236817,0.003110089,0.002052409,0.1172267],"category_scores_gemma":[0.02574629,0.0008691852,0.00217355,0.002734828,0.0006621999,0.001348909,0.002118505,0.002844246,0.06839611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001657219,"about_ca_system_score_gemma":0.003261941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07484204,"about_ca_topic_score_gemma":0.1029231,"domain_scores_codex":[0.9978811,0.0006631424,0.0002119672,0.0004690423,0.0005354857,0.0002391661],"domain_scores_gemma":[0.9901426,0.003541641,0.0005345283,0.003439529,0.001844792,0.0004968459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001544656,0.00005541646,0.002190554,0.0003736798,0.00006465206,0.00001621481,0.00007274783,0.0009989096,0.0001074225,0.001150078,0.9920653,0.00275063],"study_design_scores_gemma":[0.002017673,0.0001244376,0.02525686,0.0005734845,0.0001382689,0.00008114726,0.0007696054,0.003228448,0.001158041,0.007263811,0.9592293,0.0001590583],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007324025,0.00003179006,0.0003820174,0.0002086591,0.0000685304,0.00005576196,0.9960984,0.0005235306,0.001898909],"genre_scores_gemma":[0.005857969,0.00003422666,0.001657025,0.0001624866,0.00002403557,0.0005891969,0.9882812,0.0004762038,0.00291766],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1172267,"threshold_uncertainty_score":0.3921626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3413078548117644,"score_gpt":0.4197244791430377,"score_spread":0.07841662433127328,"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."}}