{"id":"W2288501165","doi":"10.1007/s10676-016-9388-y","title":"Building theory from consumer reactions to RFID: discovering Connective Proximity","year":2016,"lang":"en","type":"article","venue":"Ethics and Information Technology","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Personalization; Construct (python library); Marketing; Implementation; Computer science; Physical security; Business; Internet privacy; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001294501,0.00006581751,0.00008553187,0.0002125723,0.0009015004,0.00007508758,0.000190104,0.0003414742,0.00005094881],"category_scores_gemma":[0.008734213,0.00005179132,0.00001483072,0.0002495083,0.0003436166,0.002431957,0.0002110268,0.0003931185,0.00005718978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008540854,"about_ca_system_score_gemma":0.0000883375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001603837,"about_ca_topic_score_gemma":0.0005992684,"domain_scores_codex":[0.9992762,0.0001183337,0.000182999,0.0001140145,0.0001458444,0.0001626564],"domain_scores_gemma":[0.9990208,0.0004897731,0.0001026399,0.0001895329,0.0001420427,0.00005523474],"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":[0.00001449888,0.000005434812,0.0002617569,0.000005408326,0.000009193179,9.359113e-8,0.008530449,2.370333e-7,0.0008139804,0.9369234,0.0001035485,0.05333193],"study_design_scores_gemma":[0.0001957348,0.00003179394,0.0006207176,0.00004691401,0.000005716217,0.000001022309,0.007178946,0.00001004303,0.004857059,0.5472714,0.4396703,0.0001104118],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3168798,0.0001075051,0.5947355,0.07791746,0.000669204,0.0006949442,0.0001682997,0.0005651233,0.008262184],"genre_scores_gemma":[0.9961647,0.0005162846,0.002691091,0.0004934008,0.00004812738,0.00004649664,0.000004812314,0.000003038489,0.00003200493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.679285,"threshold_uncertainty_score":0.9996157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0268626699563341,"score_gpt":0.3190161399596163,"score_spread":0.2921534700032821,"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."}}