{"id":"W3020383846","doi":"10.1177/2056305120915588","title":"Unpacking the Political Effects of Social Movements With a Strong Digital Component: The Case of #IdleNoMore in Canada","year":2020,"lang":"en","type":"article","venue":"Social Media + Society","topic":"Social Media and Politics","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Social movement; Politics; Indigenous; Mainstream; Narrative; Salience (neuroscience); Political science; Unpacking; Political economy; Public administration; Public relations; Sociology; Psychology; Law","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001407185,0.0003024822,0.0003698,0.001869865,0.03076371,0.006595559,0.00145953,0.001443799,0.004985225],"category_scores_gemma":[0.003341436,0.0002591622,0.0003121155,0.003497499,0.009820819,0.001618623,0.004394356,0.002656122,0.0002131677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1154167,"about_ca_system_score_gemma":0.1191777,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9934901,"about_ca_topic_score_gemma":0.998238,"domain_scores_codex":[0.997235,0.0003872471,0.00002980402,0.0001953914,0.0004633165,0.001689317],"domain_scores_gemma":[0.9976744,0.0006492566,0.0001997962,0.00009064067,0.0004828173,0.0009029866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000165423,0.0001943316,0.09624656,0.0002378976,0.00007059561,0.00654317,0.730188,0.001093496,0.002437809,0.08665026,0.01697863,0.05919386],"study_design_scores_gemma":[0.00001310055,0.00003501826,0.1082289,0.0001840297,0.00003615457,0.0002663748,0.7898988,0.0006254124,0.000461879,0.002319062,0.09787592,0.00005540432],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9163873,0.0006021591,0.0003438699,0.009099519,0.00006395669,0.00006738533,0.0002112481,0.00001576909,0.07320888],"genre_scores_gemma":[0.9900899,0.0003308521,0.0001779687,0.0006546612,0.00001103141,0.00001399809,0.00005843385,0.00001417352,0.008648928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1154167,"threshold_uncertainty_score":0.8374108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02107784932040868,"score_gpt":0.2691304691241276,"score_spread":0.248052619803719,"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."}}