{"id":"W6968585463","doi":"10.5281/zenodo.5181074","title":"Not always invisible: finding the data about marginalized and underrepresented populations in Canada","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Analysis and Archiving","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Library and Archives Canada","funders":"","keywords":"Invisibility; Indigenous; Mainstream; Context (archaeology); Privilege (computing); Ethnic group; Poverty; Inclusion (mineral)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009974311,0.00006125614,0.0000951277,0.00008243347,0.002954219,0.0007646892,0.0009459464,0.00001714585,0.003698549],"category_scores_gemma":[0.001699601,0.00005670308,0.00001350106,0.0007724266,0.0001246595,0.0003029323,0.001663857,0.0001664265,0.00008615004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00016594,"about_ca_system_score_gemma":0.00006523285,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2826215,"about_ca_topic_score_gemma":0.3609311,"domain_scores_codex":[0.998194,0.0006935307,0.0001750144,0.0003074993,0.0003719914,0.0002579925],"domain_scores_gemma":[0.9991192,0.00008039571,0.00006011202,0.000512443,0.0001279128,0.00009999613],"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":[0.00005246215,0.0001688969,0.006667972,0.00007400235,0.0001563333,0.0001689307,0.01484619,0.0005017362,0.001631367,0.1260169,0.4767715,0.3729437],"study_design_scores_gemma":[0.0001918107,0.000004054484,0.03250769,0.00002697353,0.00001285245,0.000008542293,0.005951528,0.002784665,0.00001434546,0.0002969146,0.9581106,0.00009002766],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3696928,0.0008762704,0.006299711,0.06239641,0.0004883018,0.001201259,0.002946041,0.0005311364,0.5555681],"genre_scores_gemma":[0.9962009,0.0002962684,0.0001520906,0.000257587,0.00006519024,1.812638e-8,0.002221064,0.0001446209,0.0006623223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6265081,"threshold_uncertainty_score":0.9983438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1761593857464457,"score_gpt":0.3346049457998871,"score_spread":0.1584455600534415,"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."}}