{"id":"W4408365171","doi":"10.1177/20563051251322254","title":"Mobilization and Latency Dynamics in the #StopLine3 Discourse","year":2025,"lang":"en","type":"article","venue":"Social Media + Society","topic":"Social Media and Politics","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mobilization; Dynamics (music); Latency (audio); Political science; Computer science; Sociology; Telecommunications; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002550762,0.0003329774,0.000249128,0.002765701,0.005031518,0.006235716,0.0006127736,0.001216134,0.00598177],"category_scores_gemma":[0.01067679,0.0002674101,0.0002205096,0.002480368,0.005757719,0.005880508,0.004023594,0.001104423,0.0007682402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006082605,"about_ca_system_score_gemma":0.00250666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03711775,"about_ca_topic_score_gemma":0.05103935,"domain_scores_codex":[0.9978263,0.0009032235,0.0000852778,0.0003256902,0.0005141585,0.0003453504],"domain_scores_gemma":[0.9927663,0.004560283,0.001140121,0.0003212684,0.0007613878,0.0004505134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.000473864,0.00006697933,0.05294893,0.0002933939,0.00003175136,0.0009038951,0.8469923,0.0004561817,0.01017768,0.04874732,0.004827574,0.03408024],"study_design_scores_gemma":[0.0000358714,0.0001278979,0.09199787,0.0003860719,0.00003097721,0.0002579127,0.7303697,0.001227911,0.002761659,0.009746069,0.1629496,0.0001084755],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9417282,0.0004573653,0.00251776,0.003216323,0.00006256174,0.00005714212,0.0004581436,0.00006925534,0.05143331],"genre_scores_gemma":[0.9955609,0.0001741679,0.0004924713,0.0001793324,0.00002734642,0.00005031869,0.00017345,0.0000443715,0.003297775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03711775,"threshold_uncertainty_score":0.07380342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01858754658970427,"score_gpt":0.3462365987241587,"score_spread":0.3276490521344545,"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."}}