{"id":"W2746940567","doi":"10.1111/cag.12397","title":"Operating anew: Queering GIS with good enough software","year":2017,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"Bowdoin College","keywords":"Queer; Status quo; Politics; Sociology; Elite; Geographic information system; Field (mathematics); Gender studies; Political science; Geography; Law; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.000905707,0.0005622208,0.0006094337,0.002923446,0.01528147,0.001654182,0.001577413,0.0002935962,0.0001254175],"category_scores_gemma":[0.0006483556,0.0005483201,0.0002958982,0.00239656,0.004495997,0.001710998,0.0001275052,0.0003749904,0.00002624812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002007325,"about_ca_system_score_gemma":0.0006821263,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9591119,"about_ca_topic_score_gemma":0.9989066,"domain_scores_codex":[0.9957455,0.0001125094,0.0005879339,0.0006514026,0.0006919471,0.002210663],"domain_scores_gemma":[0.9957001,0.0001603569,0.0004869339,0.001276769,0.0007737011,0.001602193],"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":[0.00001005981,0.00001476483,0.8867822,0.00009373725,0.0004528162,0.000179623,0.0308565,0.00003936583,0.00000252418,0.07031269,0.00288065,0.008375063],"study_design_scores_gemma":[0.000626331,0.0001481772,0.3631277,0.000287545,0.00009576042,0.00005010036,0.1166906,0.000005533911,0.000008540595,0.0009144546,0.5167738,0.001271468],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8518424,0.001913672,0.00003389058,0.006468387,0.002006788,0.001399311,0.0002818086,0.0005797028,0.135474],"genre_scores_gemma":[0.9947346,0.001139113,0.001108228,0.000459011,0.0004119308,0.0002158682,0.00003555039,0.00006659391,0.001829034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5236545,"threshold_uncertainty_score":0.9996969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01296089002493622,"score_gpt":0.2266649091497937,"score_spread":0.2137040191248575,"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."}}