{"id":"W2790304980","doi":"10.1017/psrm.2018.3","title":"Elites Tweet to Get Feet Off the Streets: Measuring Regime Social Media Strategies During Protest","year":2018,"lang":"en","type":"article","venue":"Political Science Research and Methods","topic":"Social Media and Politics","field":"Social Sciences","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; National Science Foundation","keywords":"Social media; Narrative; Censorship; Rhetorical question; Media studies; Political science; Democracy; Opposition (politics); Sociology; Criticism; Political economy; Public relations; Law; Politics; Art","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0009488844,0.0002034276,0.0002249708,0.002332721,0.001012954,0.001894331,0.000294748,0.0006108443,0.002603755],"category_scores_gemma":[0.006655382,0.0002056369,0.0001448893,0.001705623,0.0005056652,0.001485456,0.0013427,0.0006416283,0.0006345746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006105519,"about_ca_system_score_gemma":0.0002894237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008200334,"about_ca_topic_score_gemma":0.01878444,"domain_scores_codex":[0.9994429,0.0001811162,0.00004435047,0.0001030375,0.0001013295,0.0001274116],"domain_scores_gemma":[0.9962243,0.001688088,0.001245677,0.0001953954,0.0003292649,0.0003172682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003054134,0.0002602847,0.91803,0.0001469753,0.00008573543,0.0002220011,0.04429149,0.0003918932,0.003593312,0.001427049,0.001544436,0.02970155],"study_design_scores_gemma":[0.000007395887,0.00007264402,0.9645225,0.00004647072,0.00002653489,0.00006979401,0.02981565,0.001014835,0.0004700756,0.0003668405,0.003571067,0.00001611208],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956583,0.00006489253,0.0002146722,0.00007185736,0.000006639566,0.00002876207,0.0003056617,0.000005365221,0.003643852],"genre_scores_gemma":[0.9980064,0.00007627615,0.0003329772,0.00002424332,0.00001449999,0.00006941539,0.0003759947,0.00000582906,0.001094392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008200334,"threshold_uncertainty_score":0.01630521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2377789804303526,"score_gpt":0.5423418810115296,"score_spread":0.304562900581177,"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."}}