{"id":"W2896271343","doi":"10.1109/pst.2017.00030","title":"Cross-National Privacy Concerns on Data Collection by Government Agencies (Short Paper)","year":2017,"lang":"en","type":"article","venue":"","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Data collection; Nationality; Government (linguistics); Warrant; Enforcement; Transparency (behavior); Internet privacy; Legislation; Information privacy; Business; Privacy policy; Public relations; Law enforcement; Personally identifiable information; Political science; Law; Sociology; Computer science; Immigration; Finance","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02277425,0.0002067704,0.0003910767,0.001119362,0.004767759,0.002971178,0.0005340285,0.001192078,0.004460895],"category_scores_gemma":[0.0460672,0.0004276738,0.0004202285,0.001786028,0.002522377,0.00252545,0.002444574,0.001442864,0.0004228901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004160952,"about_ca_system_score_gemma":0.003844893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02096578,"about_ca_topic_score_gemma":0.02152626,"domain_scores_codex":[0.9810937,0.01324142,0.001502259,0.0007464079,0.002090814,0.001325384],"domain_scores_gemma":[0.9092544,0.05312354,0.02065981,0.004660792,0.00856508,0.003736358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003593646,0.0003625444,0.3424874,0.0004798212,0.0000641724,0.0008405499,0.5808563,0.0001697513,0.002921235,0.003089071,0.003719061,0.06465071],"study_design_scores_gemma":[0.00002487699,0.0004514647,0.2242048,0.0004023738,0.0000438042,0.001220981,0.7295471,0.0003716269,0.001781889,0.00081238,0.04105441,0.00008429401],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915435,0.0003364201,0.0005839395,0.001888745,0.00002465507,0.00009805234,0.0001598625,0.00001171932,0.005352966],"genre_scores_gemma":[0.9974184,0.0003152496,0.0007434869,0.0005706868,0.00001745615,0.0001138245,0.00005928926,0.000005210595,0.0007563643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02277425,"threshold_uncertainty_score":0.1204432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1240038887396179,"score_gpt":0.3988363754660751,"score_spread":0.2748324867264572,"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."}}