{"id":"W4414973032","doi":"10.1111/cag.70039","title":"Mapping for change: Balancing big and small data in documenting environmental (in)justice in a small Canadian city","year":2025,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Environmental Justice and Health Disparities","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Trent University","funders":"","keywords":"Injustice; Environmental justice; Scholarship; Citizen journalism; Environmental studies; Big data; Economic Justice","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00604089,0.0004840938,0.0004987348,0.005680211,0.01882348,0.006745012,0.00245695,0.0007531242,0.003435463],"category_scores_gemma":[0.0169124,0.0003361715,0.0003543681,0.0103829,0.006328289,0.001999149,0.006457773,0.001342488,0.000193182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05939139,"about_ca_system_score_gemma":0.1112368,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9900813,"about_ca_topic_score_gemma":0.9973253,"domain_scores_codex":[0.9954937,0.00147464,0.0001306721,0.0004917264,0.001385703,0.001023569],"domain_scores_gemma":[0.9910653,0.002768404,0.0006103897,0.0007846325,0.003624305,0.001146941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001875079,0.0001840764,0.2795296,0.001115813,0.000166909,0.001075301,0.3649216,0.004383843,0.003774837,0.03312547,0.02927863,0.2822564],"study_design_scores_gemma":[0.00001683716,0.00006485317,0.3098602,0.00108591,0.0001011161,0.0001245617,0.5585451,0.00465723,0.001627834,0.008594252,0.1151736,0.0001486927],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8712963,0.002612919,0.01796845,0.01856744,0.0001561194,0.001123289,0.003754363,0.0001785119,0.08434266],"genre_scores_gemma":[0.9672062,0.001171421,0.02531412,0.0005407947,0.00001668274,0.0003654346,0.0006787383,0.00006059251,0.004645849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05939139,"threshold_uncertainty_score":0.4309167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04209361331395974,"score_gpt":0.2587746573482451,"score_spread":0.2166810440342854,"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."}}