{"id":"W4389151600","doi":"10.1177/14614448231213267","title":"Private attributes: The meanings and mechanisms of “privacy-preserving” adtech","year":2023,"lang":"en","type":"article","venue":"New Media & Society","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Internet privacy; Anonymity; Leverage (statistics); Attribution; Privacy by Design; Information privacy; Computer science; Privacy policy; Privacy software; Rhetoric; Private information retrieval; Computer security; Psychology; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.001988833,0.0001259324,0.0001904483,0.00002887589,0.0006553322,0.00008207478,0.001087644,0.0001942353,0.0001594163],"category_scores_gemma":[0.002243833,0.00009924091,0.0001299302,0.0008218658,0.0003116793,0.000310439,0.001092027,0.0002781271,0.00003869199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006058475,"about_ca_system_score_gemma":0.0001846987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00266816,"about_ca_topic_score_gemma":0.000792996,"domain_scores_codex":[0.9982891,0.0001503447,0.0002214596,0.0002842344,0.0006547961,0.000400078],"domain_scores_gemma":[0.9986171,0.0004798632,0.0001404385,0.000511917,0.00008245009,0.0001682572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000023715,0.00005419292,0.001782803,0.0001157747,0.0001199178,0.000003712829,0.3138744,0.00000193177,0.01543746,0.09700428,0.5597749,0.01180683],"study_design_scores_gemma":[0.0008589647,0.00006723705,0.005473689,0.00007840137,0.00006811653,0.000002641752,0.04148031,0.0005213175,0.007423368,0.6975666,0.2461309,0.0003284531],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8487047,0.001697344,0.02709042,0.1107092,0.003011241,0.002345111,0.0002071605,0.001524402,0.004710358],"genre_scores_gemma":[0.9881842,0.004323079,0.00515765,0.0007942891,0.0008892932,0.00004190724,0.00005241547,0.00002997138,0.000527156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6005623,"threshold_uncertainty_score":0.5040352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04931409435673872,"score_gpt":0.2963126704276949,"score_spread":0.2469985760709562,"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."}}