{"id":"W2963299782","doi":"10.1080/19331681.2019.1646181","title":"Diversity in Canadian election-related Twitter discourses: Influential voices and the media logic of #elxn42 and #cdnpoli hashtags","year":2019,"lang":"en","type":"article","venue":"Journal of Information Technology & Politics","topic":"Social Media and Politics","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Diversity (politics); Social media; Influencer marketing; Conversation; Politics; Democracy; Field (mathematics); Media studies; Political science; Sociology; Public relations; Computer science; World Wide Web","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":[0.005835494,0.0005769565,0.0005256608,0.007156322,0.03528351,0.01114694,0.001504855,0.001112362,0.005044655],"category_scores_gemma":[0.01691582,0.0004854554,0.0003874807,0.009274149,0.009049469,0.004145909,0.006628734,0.002082243,0.0003491862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07741938,"about_ca_system_score_gemma":0.06214602,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9675562,"about_ca_topic_score_gemma":0.9763032,"domain_scores_codex":[0.9947443,0.001133621,0.000164564,0.0005666565,0.002015471,0.001375452],"domain_scores_gemma":[0.991529,0.003530239,0.0009624328,0.0002309942,0.002558619,0.001188717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.000153059,0.00002867075,0.04427926,0.0001489705,0.00002137999,0.0004982293,0.9226761,0.0001129089,0.001243716,0.01027616,0.004208424,0.01635308],"study_design_scores_gemma":[0.00001026654,0.00002116299,0.06601001,0.0001808342,0.00002861403,0.00007664401,0.8776001,0.0003662212,0.0006743796,0.0008258165,0.05412019,0.00008568453],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9486396,0.0006171322,0.0004969088,0.006028777,0.0001087723,0.00007039974,0.0005564316,0.0000306967,0.04345143],"genre_scores_gemma":[0.9952042,0.0003635562,0.0001682694,0.0003093069,0.0000212254,0.00002614122,0.0001461574,0.00002065022,0.003740501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07741938,"threshold_uncertainty_score":0.5617195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009148127952891055,"score_gpt":0.2741490224414624,"score_spread":0.2650008944885714,"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."}}