{"id":"W3147668547","doi":"10.33621/jdsr.v3i1.49","title":"Figuring Digital Cascades: Issue Framing in Digital Media Ecosystems","year":2021,"lang":"en","type":"article","venue":"Journal of Digital Social Research","topic":"Social Media and Politics","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Institut National de la Recherche Scientifique","funders":"","keywords":"Grassroots; Government (linguistics); Digital media; Politics; Communism; Media studies; Political science; Computer science; Sociology; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001300505,0.0001643107,0.0004807547,0.0003695644,0.0006293549,0.002746567,0.0005708282,0.0003318524,0.0001649519],"category_scores_gemma":[0.01785829,0.0001680128,0.0002940081,0.001370995,0.0005852167,0.002748142,0.0001872093,0.001189888,0.000150136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008436564,"about_ca_system_score_gemma":0.002206935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002705538,"about_ca_topic_score_gemma":0.0005037498,"domain_scores_codex":[0.9949951,0.0002371838,0.0007731356,0.0002285565,0.00266352,0.001102536],"domain_scores_gemma":[0.9950396,0.002747684,0.0002287513,0.0001167076,0.001338262,0.000528979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002191372,0.00156762,0.2404576,0.0002491657,0.0003561913,0.005562264,0.4103589,0.000008319702,0.0003359696,0.03053734,0.02346409,0.2868834],"study_design_scores_gemma":[0.001339466,0.0001793689,0.003090284,0.0004084226,0.00001598401,0.0000631588,0.3822897,0.00001047333,0.0005023214,0.03831951,0.5732678,0.0005134992],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8888324,0.0006040023,0.00001343085,0.002800577,0.001168415,0.0001485715,0.0001205255,0.00002252048,0.1062895],"genre_scores_gemma":[0.9910282,0.0001247484,0.00001219284,0.00003100028,0.006443439,0.000003761736,0.00001492317,0.0000310695,0.002310628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5498037,"threshold_uncertainty_score":0.9982887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1090154369256396,"score_gpt":0.4349935784223311,"score_spread":0.3259781414966915,"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."}}