{"id":"W4412707350","doi":"10.1016/j.exis.2025.101735","title":"Socioeconomic framework and indicators for assessing cumulative effects of resource development on indigenous nations","year":2025,"lang":"en","type":"article","venue":"The Extractive Industries and Society","topic":"Mining and Resource Management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of Saskatchewan; Canadian Forest Service; Assembly of First Nations; University of Waterloo; Carleton University","funders":"Canadian Forest Service","keywords":"Indigenous; Socioeconomic status; Socioeconomic development; Resource (disambiguation); Economic growth; Development economics; Political science; Environmental resource management; Business; Natural resource economics; Economics; Sociology; Demography; Computer science; Biology; Ecology; Population","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002127167,0.00009919782,0.0001313973,0.00005743267,0.0004447142,0.00004083298,0.00005923637,0.0001361434,0.000001625514],"category_scores_gemma":[0.00005961527,0.00007645103,0.00003259445,0.0001160009,0.0001265636,0.00003502088,0.00003584382,0.0002650464,2.381757e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000770348,"about_ca_system_score_gemma":0.00003788645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003205656,"about_ca_topic_score_gemma":4.116383e-7,"domain_scores_codex":[0.9995598,0.00002205872,0.0001296686,0.0001084955,0.00005439461,0.0001255421],"domain_scores_gemma":[0.9984899,0.001343703,0.00005679287,0.00007541759,0.00001283288,0.00002140599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005333421,0.0002043394,0.009827765,0.001484165,0.003278502,0.000001393704,0.3900178,0.01447777,0.0003229488,0.03498346,0.01046553,0.534883],"study_design_scores_gemma":[0.002676751,0.0002742978,0.4221881,0.002158009,0.0005187706,0.000001847854,0.2024619,0.008633063,0.03989355,0.004176922,0.3158902,0.001126509],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883442,0.0004349597,0.007892735,0.0001619999,0.00006696031,0.0003530204,0.000004037931,0.00003381388,0.002708302],"genre_scores_gemma":[0.9985609,0.00006923729,0.0008822987,0.00008391104,0.00003036378,0.00004873069,0.000002773621,0.00000991933,0.0003118734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5337565,"threshold_uncertainty_score":0.3420427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01023139989666581,"score_gpt":0.2578050132910398,"score_spread":0.247573613394374,"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."}}