{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01754684,0.0006188119,0.0005886175,0.002302956,0.008921387,0.02005238,0.001927426,0.004263127,0.009448581],"category_scores_gemma":[0.01838652,0.0004098352,0.0005828773,0.002714439,0.09137466,0.02044296,0.01637456,0.006213367,0.001054045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00702944,"about_ca_system_score_gemma":0.005817526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005176694,"about_ca_topic_score_gemma":0.006112399,"domain_scores_codex":[0.9728587,0.0207021,0.0004836598,0.001789756,0.002577423,0.001588449],"domain_scores_gemma":[0.9818687,0.0137567,0.0009365128,0.001635193,0.0008434192,0.0009594259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.000007400048,0.000005630325,0.0001688891,0.00003334211,0.000002207623,0.0000760484,0.04005195,0.0001232646,0.00008134531,0.954374,0.001755253,0.003320779],"study_design_scores_gemma":[0.00001556844,0.00002606579,0.0003857379,0.0003346374,0.000007025038,0.0001528383,0.06563336,0.0005658214,0.0002993179,0.6249692,0.3075867,0.00002367265],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05850616,0.003006508,0.08657443,0.07865655,0.00128029,0.0002076572,0.00009312093,0.0001473191,0.771528],"genre_scores_gemma":[0.9618726,0.0009658494,0.007461047,0.003601668,0.0003132754,0.0002226771,0.00005360232,0.0001289236,0.02538048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02005238,"threshold_uncertainty_score":0.09279764,"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."}}