{"id":"W3171122582","doi":"","title":"Terms of Silence: Weaknesses in Corporate and Law Enforcement Responses to Cyberviolence against Girls","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Redress; Silence; Deference; Enforcement; Political science; Social media; Law enforcement; Identity (music); Order (exchange); Public relations; Law; Sociology; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01477761,0.0003576047,0.0004496987,0.001688376,0.01117447,0.008043796,0.002537565,0.003632849,0.004719187],"category_scores_gemma":[0.05734507,0.0004779024,0.0003685661,0.0012805,0.01327979,0.005679532,0.006667687,0.005074478,0.001029856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004396833,"about_ca_system_score_gemma":0.004827038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02412256,"about_ca_topic_score_gemma":0.03075615,"domain_scores_codex":[0.9827527,0.009416815,0.000788241,0.00161404,0.003887701,0.001540532],"domain_scores_gemma":[0.9549044,0.03122366,0.00451459,0.002970127,0.00439417,0.001992923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005682358,0.00009927567,0.03109287,0.0001778227,0.00003159435,0.001292391,0.8160831,0.00008538621,0.002077057,0.03366539,0.007510266,0.107828],"study_design_scores_gemma":[0.00001443818,0.0001549965,0.02818191,0.0006204944,0.00004382124,0.001784219,0.8551587,0.0005810444,0.003682512,0.006422329,0.1032852,0.00007035945],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7901084,0.001315068,0.00892859,0.05335447,0.0005155183,0.0001518898,0.00007986354,0.000212848,0.1453333],"genre_scores_gemma":[0.9853953,0.0003461164,0.0005737367,0.004136096,0.0000856822,0.00006892924,0.00002435161,0.00008064819,0.009289307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02412256,"threshold_uncertainty_score":0.07815242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01536050610470249,"score_gpt":0.2552714916416871,"score_spread":0.2399109855369846,"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."}}