{"id":"W4381799546","doi":"10.14324/111.444/ucloe.000060","title":"Decolonising Canadian water governance: lessons from Indigenous case studies","year":2023,"lang":"en","type":"article","venue":"UCL Open Environment","topic":"Water Governance and Infrastructure","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"McMaster University","keywords":"Indigenous; Sanitation; Corporate governance; Political science; Government (linguistics); Praxis; Politics; Economic growth; Public administration; Environmental planning; Sociology; Business; Geography; Engineering; Economics; Ecology; Law; Environmental engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"category_scores_codex":[0.0003499088,0.0001346284,0.0001876389,0.00002739972,0.001417207,0.0002022543,0.0004771721,0.00009521397,0.00155484],"category_scores_gemma":[0.00002510338,0.0001061923,0.00003932761,0.00008591,0.0001841226,0.000346392,0.0003344768,0.0001228288,0.001600045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005222,"about_ca_system_score_gemma":0.0001319344,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6562755,"about_ca_topic_score_gemma":0.6397628,"domain_scores_codex":[0.9984994,0.0000833162,0.0001613449,0.0003180958,0.0002943032,0.0006435734],"domain_scores_gemma":[0.999432,0.00003728852,0.00005820291,0.0002534169,0.000008041917,0.0002110839],"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":[0.00002812651,0.00007688287,0.04418825,0.00001432585,0.0004387248,0.01441576,0.8024789,0.001493279,0.0008442198,0.002440126,0.08485296,0.04872844],"study_design_scores_gemma":[0.0002715563,0.00002714914,0.0320095,0.00002007252,0.00002764357,0.00001619061,0.01995708,0.000004893973,0.001218421,0.002917204,0.9432433,0.0002870056],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864208,0.0002446495,0.000001840286,0.006743531,0.0004357888,0.000431828,0.0001989979,0.00002973965,0.005492806],"genre_scores_gemma":[0.990976,0.001723228,0.0001701603,0.0006513727,0.0001977071,0.00004421664,0.00003396511,0.00001892251,0.00618441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8583903,"threshold_uncertainty_score":0.9998828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04691374758707246,"score_gpt":0.3337880708892318,"score_spread":0.2868743233021594,"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."}}