{"id":"W4281628329","doi":"10.29173/assert39","title":"Using Economic Analysis to Incorporate Reparations for Black Americans into the US History Classroom","year":2022,"lang":"en","type":"article","venue":"Annals of Social Studies Education Research for Teachers","topic":"Educator Training and Historical Pedagogy","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Civil rights; Political science; Law; Politics; Government (linguistics); Civil society; Curriculum; State (computer science); Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004966714,0.0000887701,0.0002851062,0.000397972,0.004668828,0.0000271702,0.0004078235,0.00004159335,0.00009539126],"category_scores_gemma":[0.00136147,0.00009025616,0.0002748439,0.0009693204,0.001316808,0.00007576443,0.00008500098,0.0002130946,0.000003605439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003426228,"about_ca_system_score_gemma":0.003984487,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02810877,"about_ca_topic_score_gemma":0.01048168,"domain_scores_codex":[0.9977838,0.0007576247,0.0003212043,0.000296241,0.0004216761,0.0004194806],"domain_scores_gemma":[0.9977327,0.0009357498,0.0002388806,0.0001728112,0.0007930297,0.0001268289],"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":[0.00003902022,0.000141666,0.000732997,0.000009341447,0.0005129948,2.179193e-8,0.5657411,0.0009226291,0.00002045838,0.02414183,0.4047105,0.003027414],"study_design_scores_gemma":[0.00003753883,0.000090345,0.0002437661,0.000001051072,0.00006504531,8.823836e-9,0.4150813,0.0000401629,0.000002117758,0.003141038,0.5812348,0.00006286352],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8636786,0.002367205,0.0002448623,0.1217609,0.001386667,0.001634932,0.00007680537,0.00003976433,0.008810218],"genre_scores_gemma":[0.9850225,0.0001814112,0.001086476,0.0009668694,0.0008429663,0.001539682,0.00002892518,0.00001948377,0.01031171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1765243,"threshold_uncertainty_score":0.996627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6842997206055574,"score_gpt":0.6108700933371736,"score_spread":0.07342962726838387,"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."}}