{"id":"W4399325713","doi":"10.1177/08404704241257144","title":"Towards abundant intelligences: Considerations for Indigenous perspectives in adopting artificial intelligence technology","year":2024,"lang":"en","type":"article","venue":"Healthcare Management Forum","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Indigenous; Transformative learning; Health care; Multidisciplinary approach; Equity (law); Engineering ethics; Inclusion (mineral); Politics; Sociology; Political science; Knowledge management; Computer science; Engineering; Social science; Pedagogy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007248296,0.0002547716,0.0003646152,0.001335444,0.0003883326,0.0001358702,0.0001404162,0.0002306434,0.0001279261],"category_scores_gemma":[0.0003307551,0.0002489483,0.0001336467,0.001279734,0.000171824,0.0001682558,0.00007692679,0.0004617243,0.0001099769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006832253,"about_ca_system_score_gemma":0.0005612965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001139767,"about_ca_topic_score_gemma":0.002746458,"domain_scores_codex":[0.9970911,0.00005299128,0.0009955793,0.0007360593,0.0002826156,0.0008416611],"domain_scores_gemma":[0.9987775,0.0002777059,0.00009124298,0.0003795565,0.0003092932,0.0001647173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006196823,0.0001618711,0.001193754,0.001198389,0.00004860813,0.0000930332,0.01065169,0.00006022972,0.00001414319,0.3924508,0.0002176516,0.5938479],"study_design_scores_gemma":[0.00004567974,0.001312801,0.0005782138,0.001843717,0.0001030179,0.0001435901,0.3554388,0.008288754,0.004472341,0.6119996,0.01526666,0.0005068105],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2024149,0.02753608,0.2297948,0.5169786,0.005402026,0.01288444,0.00008183275,0.001581878,0.003325403],"genre_scores_gemma":[0.9782248,0.001892062,0.01741758,0.001044315,0.0003015865,0.0008204372,0.0000326974,0.00004610566,0.0002203888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7758099,"threshold_uncertainty_score":0.9999963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1870765091425983,"score_gpt":0.4446896416098088,"score_spread":0.2576131324672106,"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."}}