{"id":"W3101029204","doi":"10.1111/conl.12774","title":"Next steps in dismantling discrimination: Lessons from ecology and conservation science","year":2020,"lang":"en","type":"article","venue":"Conservation Letters","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Oppression; Privilege (computing); Environmental ethics; Equity (law); Ecology; Sociology; Process (computing); Field (mathematics); Colonialism; Engineering ethics; Political science; Politics; Law; Engineering; Computer science; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002869856,0.0001292607,0.0001343034,0.00008812903,0.0003401452,0.0001291632,0.0002571743,0.00004407047,0.0002582229],"category_scores_gemma":[0.000157554,0.0001360332,0.00002312231,0.0006137597,0.0006362248,0.000669168,0.0003299685,0.0001098114,0.00009199369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001582468,"about_ca_system_score_gemma":0.00002367564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002272287,"about_ca_topic_score_gemma":0.001464331,"domain_scores_codex":[0.9986809,0.00006945716,0.0002474755,0.0004772074,0.0002999791,0.0002250084],"domain_scores_gemma":[0.9994783,0.0001240346,0.0001180621,0.00015323,0.00001901943,0.000107288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001776255,0.00002023927,0.9660192,0.000009833415,0.000004632499,0.000007615375,0.002840986,0.0003083315,0.02734199,0.0001298836,0.001827591,0.001471921],"study_design_scores_gemma":[0.0004917986,0.00001635589,0.9692154,0.00001220747,0.00001616132,6.273223e-7,0.001292291,0.02119756,0.0002665033,0.0001190557,0.007210599,0.0001614286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7412488,0.00001263067,0.0008245396,0.2573656,0.0000825257,0.000202354,0.000006200748,0.00003554166,0.0002218867],"genre_scores_gemma":[0.9435909,0.00002221223,0.001115119,0.05514497,0.00003519309,0.00001152275,0.0000344889,0.00000630557,0.00003924989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2023422,"threshold_uncertainty_score":0.5547271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03965200305508349,"score_gpt":0.2316632007056852,"score_spread":0.1920111976506017,"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."}}