{"id":"W2955310695","doi":"10.2478/nor-2019-0013","title":"Structural Ageism in Big Data Approaches","year":2019,"lang":"en","type":"article","venue":"Nordicom review/NORDICOM review","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Ministerio de Economía y Competitividad","keywords":"Big data; Sexual orientation; Race (biology); Raw data; Data science; Computer science; Psychology; Orientation (vector space); Sociology; Social psychology; Data mining; Mathematics; Gender studies","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005040289,0.0006199002,0.002159754,0.0002518364,0.0003163265,0.00008669364,0.004523831,0.0003722764,0.001207644],"category_scores_gemma":[0.001760153,0.0005355961,0.0003564035,0.002336015,0.0005420999,0.0007958413,0.001235205,0.001143383,0.001794866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002502275,"about_ca_system_score_gemma":0.000480763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001389837,"about_ca_topic_score_gemma":0.002905184,"domain_scores_codex":[0.9931163,0.00152089,0.001574749,0.001520673,0.001091185,0.001176233],"domain_scores_gemma":[0.9944894,0.0003282625,0.0006738368,0.004072706,0.0001261193,0.0003097056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004089035,0.00008264821,0.02755153,0.01600223,0.0000593843,0.00003351175,0.0002517946,5.157947e-7,0.000003057057,0.003167091,0.03557265,0.9172715],"study_design_scores_gemma":[0.0005312285,0.00003666636,0.00655266,0.03600671,0.000214253,0.00002863996,0.0001415501,0.00004418385,0.000001389273,0.0005589669,0.9551997,0.0006840327],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.005492334,0.9356704,0.00003852836,0.02706048,0.001359792,0.005400306,0.00006774289,0.0004454532,0.02446491],"genre_scores_gemma":[0.05354011,0.9343898,0.0007918235,0.008985643,0.0003955081,0.0002125074,0.0003067486,0.0000789674,0.001298886],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9196271,"threshold_uncertainty_score":0.9997095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1898711766133916,"score_gpt":0.350765517658552,"score_spread":0.1608943410451604,"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."}}