{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02231711,0.0006188567,0.0009562825,0.00878091,0.04611311,0.01657582,0.00565744,0.002133907,0.005332041],"category_scores_gemma":[0.06559959,0.0009501907,0.0008281474,0.02590048,0.01169062,0.005273594,0.01385079,0.003911367,0.0007586168],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.1938566,"about_ca_system_score_gemma":0.5033617,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9978468,"about_ca_topic_score_gemma":0.9986483,"domain_scores_codex":[0.9703216,0.006150786,0.001710576,0.00237104,0.01224051,0.007205592],"domain_scores_gemma":[0.9165291,0.02224888,0.003547087,0.00438599,0.03676424,0.0165247],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003253981,0.0001918775,0.1247939,0.001999227,0.000167808,0.003907388,0.4817875,0.0008091985,0.00249674,0.02324134,0.1322596,0.2280201],"study_design_scores_gemma":[0.00002777906,0.00006548679,0.09469917,0.001490987,0.00009697695,0.0004973348,0.5993062,0.0009104501,0.001335894,0.004222552,0.2970684,0.0002787126],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6078902,0.01812138,0.0075073,0.2554287,0.001466802,0.00166174,0.01478552,0.0005628456,0.09257548],"genre_scores_gemma":[0.900999,0.01616571,0.01755793,0.0258894,0.0001705543,0.0006403456,0.006074722,0.0003379871,0.03216436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9776829,"threshold_uncertainty_score":0.935012,"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."}}