{"id":"W2988359127","doi":"10.1016/j.future.2019.11.009","title":"Toward monetizing personal data: A two-sided market analysis","year":2019,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Zayed University","keywords":"Computer science; Popularity; Stochastic game; Service (business); Consistency (knowledge bases); Function (biology); Computer security; Data science; Business; Marketing; Microeconomics; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.001365561,0.0001483381,0.0002804404,0.0001947208,0.0004343411,0.0006782346,0.0007011651,0.0001369352,0.000199781],"category_scores_gemma":[0.00003894575,0.0001434574,0.00009367819,0.0006648856,0.00002874529,0.0007673959,0.0003103665,0.000148891,0.0001029922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110183,"about_ca_system_score_gemma":0.000117628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003563455,"about_ca_topic_score_gemma":0.002343528,"domain_scores_codex":[0.9976358,0.0006555744,0.0003006392,0.0005734811,0.0005668686,0.0002675941],"domain_scores_gemma":[0.998793,0.0000527373,0.0001659503,0.0007239198,0.0001501309,0.0001142367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003430451,0.00007214135,0.004638971,0.00007877173,0.0006680966,0.00001053378,0.01976889,0.001513367,0.0005299097,0.004655056,0.9616595,0.006370463],"study_design_scores_gemma":[0.0003467193,0.00002852197,0.001310653,0.00001579843,0.000109813,0.000005263093,0.001747621,0.5188347,0.00001713316,0.000008044441,0.4773311,0.0002446093],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09574571,0.004675942,0.7460833,0.004586957,0.1406607,0.001948861,0.0003565463,0.0005045886,0.005437436],"genre_scores_gemma":[0.8073817,0.0001423659,0.006209082,0.0003628712,0.1834263,0.00003735616,0.001484301,0.00001955995,0.0009365285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7398742,"threshold_uncertainty_score":0.6540232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05579319747461656,"score_gpt":0.2963300819783556,"score_spread":0.240536884503739,"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."}}