{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008357474,0.0002268724,0.0002039661,0.001222218,0.006352605,0.008731614,0.0006499301,0.001962858,0.003297905],"category_scores_gemma":[0.02789308,0.0002929058,0.0004281923,0.001020113,0.01477351,0.01076299,0.005250953,0.00284144,0.0002396809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002055299,"about_ca_system_score_gemma":0.002049243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001706717,"about_ca_topic_score_gemma":0.002454689,"domain_scores_codex":[0.9853475,0.01096357,0.0003254005,0.0005148592,0.001946388,0.0009024047],"domain_scores_gemma":[0.9467543,0.03365853,0.0117362,0.004175383,0.001568702,0.002106897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001980635,0.0004311168,0.1419069,0.0003574885,0.0001445544,0.0024353,0.4351154,0.001100996,0.002340898,0.2801023,0.006694831,0.1291722],"study_design_scores_gemma":[0.00002510494,0.0004129811,0.1014733,0.001264159,0.00009029649,0.004893882,0.5476452,0.002924511,0.00395267,0.141488,0.1956887,0.0001410876],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8624098,0.003239256,0.009456915,0.04139649,0.0002153184,0.00003875983,0.00008095611,0.00005182431,0.08311073],"genre_scores_gemma":[0.9976704,0.0004387916,0.0003946302,0.0007097053,0.00005997275,0.00000740615,0.00001126796,0.000008212094,0.000699718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008731614,"threshold_uncertainty_score":0.04419911,"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."}}