{"id":"W3018045673","doi":"10.1177/2056305120915618","title":"Cybervetting and the Public Life of Social Media Data","year":2020,"lang":"en","type":"article","venue":"Social Media + Society","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Toronto Metropolitan University","funders":"Canada Research Chairs; Ryerson University","keywords":"Social media; Context (archaeology); Set (abstract data type); Internet privacy; Public relations; Survey data collection; Private information retrieval; Information privacy; Social psychology; Psychology; Sociology; Political science; Computer science; World Wide Web","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":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.003009774,0.0001514981,0.0003695806,0.00001501794,0.001982162,0.0001749133,0.001372022,0.0003049083,0.0001703302],"category_scores_gemma":[0.01948914,0.0001284455,0.0001952517,0.0005843365,0.002271357,0.0006456305,0.001250152,0.0004892391,0.00001314445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005973318,"about_ca_system_score_gemma":0.0005122193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003065522,"about_ca_topic_score_gemma":0.004546143,"domain_scores_codex":[0.9972853,0.0006756911,0.0003603716,0.0003944913,0.0008486948,0.0004354247],"domain_scores_gemma":[0.9976145,0.001410114,0.0002997839,0.0002695835,0.0001586446,0.0002473828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003074383,0.00003054044,0.0001972627,0.00004343129,0.00008385459,6.137594e-7,0.8274882,1.035838e-8,0.00004254935,0.05089334,0.1122922,0.008897248],"study_design_scores_gemma":[0.003622164,0.00001927638,0.002112622,0.00001448093,0.0002036263,8.348738e-7,0.747963,0.000343456,0.00002897794,0.02638586,0.2188008,0.0005048632],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2750224,0.004088997,0.0008952678,0.701538,0.003863441,0.001656458,0.001547147,0.0005842046,0.01080404],"genre_scores_gemma":[0.9897872,0.000740374,0.0003130051,0.001878756,0.007072756,0.00002064357,0.0001623327,0.0000183163,0.000006615861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7147648,"threshold_uncertainty_score":0.9993171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1031858463909835,"score_gpt":0.3186561402659899,"score_spread":0.2154702938750064,"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."}}