{"id":"W1982333844","doi":"10.1177/0165551505055400","title":"How much is too little? Privacy and smart cards in Hong Kong and Ontario","year":2005,"lang":"en","type":"article","venue":"Journal of Information Science","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Conceptualization; Identity (music); Internet privacy; ICTS; China; Sociology; Focus (optics); Information privacy; Public relations; Information and Communications Technology; Political science; Business; Computer science; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00291091,0.00005740719,0.0001050298,0.0003506795,0.0004090932,0.0008239447,0.0003435558,0.00004749697,0.00001415143],"category_scores_gemma":[0.001181844,0.00004932293,0.00001999145,0.0004789376,0.0003333309,0.01586249,0.0001374742,0.0002011972,0.000003202733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003205638,"about_ca_system_score_gemma":0.0006520099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003790637,"about_ca_topic_score_gemma":0.007807043,"domain_scores_codex":[0.9987399,0.00003390957,0.0002983263,0.00006969338,0.0006697309,0.0001884415],"domain_scores_gemma":[0.9991559,0.00003468168,0.0002816866,0.0001008515,0.0002844851,0.0001423704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00006690127,0.00005715367,0.2127059,0.00004085354,0.00001007378,0.00000370352,0.5143126,0.00001310756,0.0005613893,0.009014558,0.005792879,0.2574209],"study_design_scores_gemma":[0.0009885471,0.000136298,0.5157191,0.00008576815,0.000009473027,0.00008599753,0.02340347,0.0007158739,0.001400151,0.004072397,0.4531439,0.0002391348],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835094,0.00009168794,0.0008925215,0.01130413,0.0002111288,0.0001102075,0.000002047739,0.000006387328,0.003872475],"genre_scores_gemma":[0.9969548,0.0002572815,0.002197585,0.000388244,0.00009536233,8.462946e-7,3.243253e-7,9.688275e-7,0.0001045704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4909091,"threshold_uncertainty_score":0.9979022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02259539456538074,"score_gpt":0.2899245693904535,"score_spread":0.2673291748250727,"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."}}