{"id":"W3101039898","doi":"10.22215/etd/2016-11409","title":"Deleuze and Big Data: How Facebook's Use of Big Data Analytics Shifts Legal Personhood, Privacy and Commercial Expression","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Instrumentalism; Big data; Assemblage (archaeology); Legal aspects of computing; Personhood; Analytics; Internet privacy; Data science; Determinism; Population; Sociology; Computer science; World Wide Web; Epistemology; The Internet; Political science; Law; Data mining; Geography; Philosophy","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.01503975,0.0005144189,0.0003937598,0.002439997,0.01238591,0.02107943,0.00149147,0.004142046,0.004029534],"category_scores_gemma":[0.02767142,0.0005480685,0.0006148073,0.002065312,0.05835341,0.02290009,0.0116347,0.006562676,0.0006459456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005786451,"about_ca_system_score_gemma":0.004887305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00793831,"about_ca_topic_score_gemma":0.006413115,"domain_scores_codex":[0.981167,0.01325859,0.0003618533,0.001639951,0.002756561,0.000815913],"domain_scores_gemma":[0.980115,0.01451603,0.001118881,0.00247681,0.001161074,0.0006123446],"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":[0.00001724881,0.00001601255,0.001507271,0.00003577454,0.000007708843,0.0002697553,0.1116381,0.0001436458,0.0003934588,0.8730441,0.002388878,0.01053798],"study_design_scores_gemma":[0.00002948308,0.00005218703,0.002657059,0.0004814526,0.00002243541,0.0008111971,0.09363626,0.001688163,0.001875267,0.6017774,0.2968861,0.00008294293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2358215,0.00654877,0.08238406,0.2058878,0.0009096038,0.0001915187,0.0001885482,0.0002128506,0.4678553],"genre_scores_gemma":[0.9771358,0.001562308,0.00515674,0.005425622,0.0001505968,0.000126575,0.0000423083,0.00008549535,0.01031469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9876141,"threshold_uncertainty_score":0.07953876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3629657982379585,"score_gpt":0.4097584678480804,"score_spread":0.04679266961012185,"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."}}