{"id":"W2897354250","doi":"10.1177/1473779518802571","title":"In search of a privacy action against breaches of physical privacy in Hong Kong","year":2018,"lang":"en","type":"article","venue":"Common Law World Review","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Confidentiality; Punitive damages; Tort; Damages; Statutory law; Internet privacy; Privacy laws of the United States; Personally identifiable information; Law; Cause of action; Privacy policy; Action (physics); Seclusion; Privacy law; Information privacy; Political science; Business; Computer security; Plaintiff; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001609993,0.0001154768,0.0004927998,0.0001452217,0.0001162763,0.00002052025,0.0006519029,0.00005303148,0.00005693259],"category_scores_gemma":[0.0003180606,0.0001084457,0.0001015036,0.001278462,0.0005202964,0.000581693,0.0003551918,0.0002825122,0.0000214133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001201971,"about_ca_system_score_gemma":0.00009468239,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00589328,"about_ca_topic_score_gemma":0.03217657,"domain_scores_codex":[0.9979651,0.000722373,0.0004587682,0.0002322756,0.0003580013,0.0002634806],"domain_scores_gemma":[0.9989694,0.0001656769,0.0001912746,0.0005170096,0.00009226153,0.00006438408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002092948,0.003376773,0.0545917,0.01570061,0.00007475452,0.00001862314,0.0474492,0.000004227213,0.006919556,0.2473557,0.006257247,0.6180423],"study_design_scores_gemma":[0.001985409,0.0004146097,0.0470884,0.03060777,0.0001043454,0.000003135947,0.001247382,0.0003906971,0.02602966,0.0349483,0.8561863,0.0009939937],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9422521,0.01916541,0.00003775067,0.006381985,0.0002810057,0.001919351,0.00001474646,0.00005748627,0.02989019],"genre_scores_gemma":[0.9866878,0.01266575,0.0001175541,0.0002110519,0.0002179576,0.00002482219,0.000008317624,0.000009287509,0.00005746495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.849929,"threshold_uncertainty_score":0.9854837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09189568260883578,"score_gpt":0.3981252106295614,"score_spread":0.3062295280207256,"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."}}