{"id":"W3041031934","doi":"","title":"Return on Data: Personalizing Consumer Guidance in Data Exchanges","year":2019,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Business; Negotiation; Data Protection Act 1998; Order (exchange); Internet privacy; Information privacy; Consumer privacy; Personally identifiable information; Marketing; Computer security; Finance; Law; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.04289385,0.0003839814,0.0004003164,0.001805215,0.007402188,0.01485894,0.002193508,0.01556907,0.01101714],"category_scores_gemma":[0.1075504,0.0006694407,0.0008969448,0.002137952,0.02797026,0.02403073,0.01435128,0.008720226,0.001795863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00629996,"about_ca_system_score_gemma":0.009416213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00896275,"about_ca_topic_score_gemma":0.006587634,"domain_scores_codex":[0.9557809,0.03071865,0.001447258,0.003018299,0.006795947,0.002239003],"domain_scores_gemma":[0.9205441,0.05684908,0.004882812,0.01099868,0.004829738,0.001895666],"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":[0.0001150938,0.00008388472,0.005815112,0.00008023527,0.0000213222,0.000482032,0.02724917,0.0004314531,0.0004840925,0.9034113,0.0175973,0.04422888],"study_design_scores_gemma":[0.00009647256,0.0001224643,0.00447841,0.0005890788,0.00005577299,0.001066984,0.02052718,0.003138217,0.001729729,0.6309636,0.3371233,0.0001089319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07817055,0.00220459,0.06618029,0.1838685,0.0002984053,0.000358485,0.0001650968,0.0003483126,0.6684058],"genre_scores_gemma":[0.9292902,0.001107409,0.01902115,0.03040981,0.0002819268,0.0002562341,0.0001043325,0.0001507348,0.01937816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04289385,"threshold_uncertainty_score":0.2268471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07514811823093458,"score_gpt":0.3337550178686887,"score_spread":0.2586068996377541,"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."}}