{"id":"W4391054301","doi":"10.1177/20563051231224723","title":"Disinformation-for-Hire as Everyday Digital Labor: Introduction to the Special Issue","year":2024,"lang":"en","type":"article","venue":"Social Media + Society","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Carnegie Corporation of New York","keywords":"Disinformation; Context (archaeology); Politics; Everyday life; Digital media; Political science; Production (economics); Sociology; Social media; Economics; Law","routes":{"ca_aff":true,"ca_fund":false,"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":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006370455,0.0001249272,0.0001323899,0.00002919159,0.00117292,0.001112149,0.0002280444,0.0001540717,0.0003714894],"category_scores_gemma":[0.0003549403,0.0001045143,0.0002761242,0.0005826158,0.0002394896,0.002190997,0.00002495421,0.0001705272,0.001072957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000278317,"about_ca_system_score_gemma":0.0003045379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001873547,"about_ca_topic_score_gemma":0.0008805524,"domain_scores_codex":[0.9987869,0.0000283788,0.0002648609,0.0001914397,0.0003860676,0.0003423293],"domain_scores_gemma":[0.9993623,0.0002807517,0.00004490375,0.00008676303,0.0001182507,0.0001070493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007550679,0.000008810764,0.000004787639,0.00001527537,0.00002510421,9.055645e-8,0.3395118,0.00000509464,4.489429e-7,0.06871136,0.4201323,0.1715773],"study_design_scores_gemma":[0.0000886199,0.00001482535,0.00004815442,0.00001091035,0.00001595398,2.937044e-7,0.09993227,0.00002869747,0.00001297323,0.009781189,0.8899347,0.0001314624],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.05512941,0.0003444463,0.002867277,0.5255464,0.02894529,0.002500245,0.0009423869,0.0008938372,0.3828307],"genre_scores_gemma":[0.8194352,0.0002159097,0.0001368402,0.00221135,0.1707833,0.0001869137,0.0004150965,0.00003239709,0.006583042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7643058,"threshold_uncertainty_score":0.9999248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008473250937130234,"score_gpt":0.2604946537355261,"score_spread":0.2520214027983959,"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."}}