{"id":"W6958121520","doi":"10.6084/m9.figshare.12848378.v1","title":"Raining on the parties’ parade: how media storms disrupt the electoral communicational environment","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Social Media and Politics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Salience (neuroscience); Politics; Normative; Variety (cybernetics); Political communication; Exploit; Storm","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":[0.002117752,0.0001642177,0.0002649687,0.002109666,0.00263006,0.004549002,0.0004444102,0.0005539216,0.004054084],"category_scores_gemma":[0.01036423,0.0001989489,0.0002197819,0.003172992,0.001567008,0.002254568,0.001810563,0.0009889718,0.0006888381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004174106,"about_ca_system_score_gemma":0.002632573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3021102,"about_ca_topic_score_gemma":0.5337951,"domain_scores_codex":[0.9987832,0.0003976893,0.00004649913,0.0001825423,0.0003017582,0.0002883901],"domain_scores_gemma":[0.9945765,0.002756323,0.001011901,0.0004791613,0.0008015195,0.0003746777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006340423,0.0001712179,0.7674142,0.0003214157,0.0001272026,0.0007483311,0.1034907,0.001191371,0.003450417,0.01279454,0.0291822,0.08047441],"study_design_scores_gemma":[0.00001756057,0.00004277128,0.8950489,0.0001038897,0.00004183155,0.00009327275,0.0542408,0.001815155,0.001048121,0.002062306,0.04544069,0.00004465866],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677445,0.0003751408,0.001000011,0.00195016,0.00006187636,0.00007539618,0.003208653,0.00006439537,0.02551986],"genre_scores_gemma":[0.9955965,0.0001822785,0.0005030247,0.0001785497,0.00003605377,0.00003264987,0.001386074,0.00003273751,0.002052144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3021102,"threshold_uncertainty_score":0.6007035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2157358033522956,"score_gpt":0.3215212843505,"score_spread":0.1057854809982045,"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."}}