{"id":"W3201832427","doi":"10.1177/25148486211043503","title":"A political ecology of data","year":2021,"lang":"en","type":"article","venue":"Environment and Planning E Nature and Space","topic":"Water Governance and Infrastructure","field":"Social Sciences","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Materiality (auditing); Politics; Political ecology; Scholarship; Corporate governance; Environmental governance; Praxis; Environmental ethics; Argument (complex analysis); Sociology; Representation (politics); Environmental politics; Metadata; Political science; Data science; Environmental resource management; Computer science; Economics; Law; World Wide Web; Management","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":[0.0001034299,0.00004404864,0.00008434167,0.000009524244,0.00008884155,0.00001439401,0.00007017354,0.0001502462,0.00007414829],"category_scores_gemma":[0.00004993411,0.00003814094,0.000007424901,0.00002316816,0.0001339306,0.000101067,0.00009794223,0.0001649413,0.000001010587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007234857,"about_ca_system_score_gemma":0.00002252161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000476385,"about_ca_topic_score_gemma":0.0000322946,"domain_scores_codex":[0.9995269,0.00003642915,0.00004872157,0.0001474536,0.0001017116,0.0001388494],"domain_scores_gemma":[0.9997461,0.00005994382,0.0000237095,0.0001089769,0.000003914723,0.00005731218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002221486,0.00003236821,0.7705144,0.00002615828,0.00004076583,0.00008353053,0.00623099,0.00000346225,0.0009769927,0.1964544,0.02379706,0.001817642],"study_design_scores_gemma":[0.0002130422,0.00002932412,0.3400581,0.00002164354,0.00002300645,0.000007839074,0.003680468,0.00001782448,0.0005877842,0.00362623,0.6516289,0.0001058439],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732144,0.005117959,0.00003806481,0.005747726,0.0001290245,0.00004347455,0.00004747954,0.000007404667,0.01565442],"genre_scores_gemma":[0.9968883,0.0004366052,0.0008426515,0.0003635823,0.0001538076,3.01148e-7,0.00002407462,0.000002306743,0.00128838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6278318,"threshold_uncertainty_score":0.1555343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01607952541370118,"score_gpt":0.2854686626270048,"score_spread":0.2693891372133037,"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."}}