{"id":"W4318769152","doi":"10.32920/21992072","title":"Cybervetting and the Public Life of Social Media Data","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Toronto Metropolitan University","funders":"Canada Research Chairs","keywords":"Social media; Context (archaeology); Set (abstract data type); Public relations; Internet privacy; Private life; Survey data collection; Private information retrieval; Psychology; Social psychology; Sociology; Political science; Computer science","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":[0.006836897,0.0001990884,0.0001903362,0.001007508,0.00569964,0.008796429,0.0005493755,0.001868981,0.003920156],"category_scores_gemma":[0.02367263,0.0002723893,0.0003670107,0.001108239,0.01451119,0.009918861,0.004341491,0.002303082,0.0002333035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001993452,"about_ca_system_score_gemma":0.00182215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001846183,"about_ca_topic_score_gemma":0.002143238,"domain_scores_codex":[0.9885321,0.008587264,0.0002573091,0.0004405642,0.00147892,0.0007037877],"domain_scores_gemma":[0.9551156,0.03027096,0.008814881,0.003302224,0.001037264,0.001459073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002050665,0.0003288095,0.1085314,0.0003493017,0.0001005907,0.002094256,0.3980632,0.001126722,0.002182072,0.3482392,0.007810666,0.1309687],"study_design_scores_gemma":[0.00002641662,0.0003516579,0.1047377,0.0009956195,0.00006928641,0.003755852,0.5088763,0.003274973,0.004070849,0.1805678,0.1931519,0.0001216685],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.829235,0.003524382,0.01041115,0.04476475,0.0001947306,0.00004012867,0.0001095507,0.00006960418,0.1116506],"genre_scores_gemma":[0.9976565,0.0004338372,0.0003795438,0.0005358996,0.00005237592,0.00000755536,0.00001163709,0.000009549541,0.0009131409],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.008796429,"threshold_uncertainty_score":0.03615737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1768857173901264,"score_gpt":0.3624897666993109,"score_spread":0.1856040493091846,"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."}}