{"id":"W4394931610","doi":"10.1177/08944393241247420","title":"Covering the Campaign: Computational Tools for Measuring Differences in Candidate and Party News Coverage With Application to an Emerging Democracy","year":2024,"lang":"en","type":"article","venue":"Social Science Computer Review","topic":"Media Influence and Politics","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Harvard Academy for International and Area Studies, Harvard University; McGill University; United States Agency for International Development","keywords":"Democracy; Political science; Computer science; Data science; Advertising; Public relations; Business; Politics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001689709,0.00009830603,0.0001866147,0.00005656815,0.001051408,0.0006111666,0.0003516609,0.00002415709,0.000007410903],"category_scores_gemma":[0.0001143182,0.00006713682,0.00002943552,0.0008127032,0.0003475797,0.0008110723,0.00005824006,0.00009414751,0.000004403496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000119471,"about_ca_system_score_gemma":0.0004424172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006233127,"about_ca_topic_score_gemma":0.0007577521,"domain_scores_codex":[0.9985473,0.0001182998,0.0002096926,0.0003166573,0.0004550463,0.00035297],"domain_scores_gemma":[0.9992998,0.0003202557,0.00005397762,0.00009044987,0.00009581297,0.0001397217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004978859,0.00001756718,0.009132576,0.0005442986,0.000008581555,0.00000258081,0.03136637,0.0002850297,0.00001942866,0.1449463,0.0002397137,0.8134325],"study_design_scores_gemma":[0.001132583,0.0006206832,0.1713384,0.0135469,0.000254832,0.00002348343,0.008965141,0.07120989,0.00003881921,0.1302953,0.599955,0.002619028],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6350767,0.007376872,0.3152074,0.03323287,0.0009800124,0.005441034,0.00004375157,0.0002111514,0.002430214],"genre_scores_gemma":[0.9931005,0.001896903,0.001592797,0.002813357,0.0004373366,0.0001392316,0.000004552197,0.000006239138,0.000009135364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8108135,"threshold_uncertainty_score":0.8086686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07124864682663774,"score_gpt":0.3742817122047396,"score_spread":0.3030330653781019,"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."}}