{"id":"W2047096265","doi":"10.1007/s12394-010-0067-6","title":"A pragmatic approach to privacy risk optimization: privacy by design for business practices","year":2010,"lang":"en","type":"article","venue":"Identity in the Information Society","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Privacy Analytics (Canada)","funders":"","keywords":"Privacy by Design; Information privacy; Standardization; Privacy policy; Process (computing); Privacy software; Computer science; Privacy laws of the United States; Business process; Computer security; Business; Internet privacy; Work in process; Marketing","routes":{"ca_aff":true,"ca_fund":false,"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":[0.04009767,0.001667894,0.001246921,0.003279068,0.005791584,0.01269016,0.003177089,0.007211675,0.005368075],"category_scores_gemma":[0.04273963,0.001346474,0.002221692,0.001930927,0.03724066,0.01793157,0.01061792,0.008879425,0.001075687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008484345,"about_ca_system_score_gemma":0.01085504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002370764,"about_ca_topic_score_gemma":0.001694034,"domain_scores_codex":[0.9467058,0.03820377,0.001679067,0.003048277,0.008920744,0.001442438],"domain_scores_gemma":[0.9671181,0.02082195,0.001906823,0.006248114,0.002961906,0.0009431433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000005148072,0.00001249826,0.00005405189,0.00003628132,0.000006269337,0.00002320822,0.0006240096,0.000667075,0.00007918162,0.9944662,0.0005069482,0.003519134],"study_design_scores_gemma":[0.00001940682,0.00002703773,0.00004300764,0.00008887161,0.000008034889,0.00006347934,0.0003148468,0.002837706,0.0003330269,0.9712706,0.02497976,0.00001406429],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002749308,0.0008577902,0.9126642,0.01962857,0.0001826656,0.0002998481,0.00004456652,0.0001238962,0.06344912],"genre_scores_gemma":[0.3462909,0.001314587,0.6330813,0.004183863,0.000424279,0.001753026,0.00008411635,0.0002378606,0.01263008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04009767,"threshold_uncertainty_score":0.2120593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03471304903168269,"score_gpt":0.3322943754177607,"score_spread":0.297581326386078,"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."}}