{"id":"W3010858696","doi":"10.1111/area.12622","title":"Power in numbers/Power and numbers: Gentle data activism as strategic collaboration","year":2020,"lang":"en","type":"article","venue":"Area","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Research Foundation","keywords":"Grassroots; Action (physics); Power (physics); Context (archaeology); Militant; Sociology; Politics; Agonism; Corporate governance; Collective action; Normative; Public relations; Political science; Law; Economics","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003547644,0.00007589353,0.0001175388,0.00004104705,0.0002107368,0.0001415948,0.0001884873,0.00006193459,0.0002957957],"category_scores_gemma":[0.0001399646,0.00007495697,0.00001247614,0.0005356981,0.00009026389,0.0008666229,0.0001035918,0.00007433921,0.0001749348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000275093,"about_ca_system_score_gemma":0.0001122238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002922546,"about_ca_topic_score_gemma":0.007715533,"domain_scores_codex":[0.9991153,0.00006618349,0.0001793576,0.0001839047,0.0002802876,0.0001749589],"domain_scores_gemma":[0.9995244,0.00004634309,0.00008566609,0.0001688431,0.00009219362,0.00008259995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0000530491,0.00008397234,0.1488784,0.00004590404,0.0001269595,0.00002649918,0.7023488,0.00001731691,0.0001098885,0.09342059,0.05456476,0.0003238331],"study_design_scores_gemma":[0.0008216761,0.00008097415,0.01696281,0.00003800969,0.00001343651,0.00000252818,0.6841931,0.0002309835,0.00002106742,0.002384176,0.2949026,0.0003487278],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6290237,0.0001217464,0.00002153758,0.007791904,0.0002342791,0.0003073339,0.00003647748,0.00005715167,0.3624059],"genre_scores_gemma":[0.9990396,0.00006766729,0.0000486508,0.0005296664,0.00004279279,0.000009180976,0.00002394349,0.000004480701,0.0002339915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3700159,"threshold_uncertainty_score":0.4418035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07139440275831609,"score_gpt":0.329741401131408,"score_spread":0.2583469983730919,"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."}}