{"id":"W2947696923","doi":"10.1108/jd-09-2018-0143","title":"Emerging practices for managing user misconduct in online news media comments sections","year":2019,"lang":"en","type":"article","venue":"Journal of Documentation","topic":"Social Media and Politics","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Offensive; Originality; Public relations; Narrative; Value (mathematics); Misconduct; Sociology; Journalism; Work (physics); User-generated content; Social media; Knowledge management; Political science; Creativity; Psychology; World Wide Web; Computer science; Media studies; Social psychology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004201236,0.00004587676,0.000109982,0.0001395405,0.0001070529,0.00007307075,0.00009207229,0.00003927638,0.0001334795],"category_scores_gemma":[0.0005853809,0.00004508458,0.00005092537,0.0001623754,0.00002192852,0.0009918991,0.000006785558,0.0001233105,0.0000060449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001641237,"about_ca_system_score_gemma":0.0001186338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002578832,"about_ca_topic_score_gemma":0.004440418,"domain_scores_codex":[0.9991094,0.0001384743,0.0002854431,0.00005484286,0.0002626359,0.0001492015],"domain_scores_gemma":[0.9986428,0.0005248797,0.0006228693,0.00003573226,0.0001127542,0.00006096223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.000166485,0.0004406408,0.6444286,0.00008961665,0.0001693315,0.00001083848,0.2869677,0.0003607655,0.003103327,0.01586447,0.009138224,0.03925997],"study_design_scores_gemma":[0.00287177,0.0001765309,0.01338879,0.0001592149,0.0001169598,0.000004656379,0.6917544,0.00007081551,0.0006788861,0.0174624,0.2731258,0.0001897733],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846712,0.00007226117,0.0001205075,0.01155333,0.002116045,0.0002095408,0.00000325019,0.000004952266,0.001248933],"genre_scores_gemma":[0.9945985,0.0003259705,0.002848546,0.0006124244,0.001050415,0.00000421495,0.000009271552,0.00000772184,0.0005429154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6310398,"threshold_uncertainty_score":0.3898439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06653161152201145,"score_gpt":0.4472581973129245,"score_spread":0.380726585790913,"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."}}