{"id":"W4289515440","doi":"10.3233/ip-211535","title":"Culling the FLoC: Market forces, regulatory regimes and Google’s (mis)steps on the path away from targeted advertising1","year":2022,"lang":"en","type":"article","venue":"Information Polity","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"European Commission","keywords":"Business; Path (computing); Collation; Ideology; Marketing; Advertising; Computer science; Political science; Law; Politics","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001338608,0.00009093525,0.00008912515,0.00004018511,0.001900488,0.0002857647,0.0002522217,0.00004445686,0.0004419428],"category_scores_gemma":[0.0001070954,0.00006300859,0.00005556619,0.0001683907,0.0001787252,0.001873598,0.00003327483,0.0001899308,0.00002461209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001632218,"about_ca_system_score_gemma":0.0001397727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00189465,"about_ca_topic_score_gemma":0.0001992395,"domain_scores_codex":[0.9988023,0.0002233844,0.0003175209,0.00008278704,0.0003608964,0.0002131262],"domain_scores_gemma":[0.9992452,0.0002661258,0.0001826001,0.0002048191,0.00004090681,0.00006041115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001371214,0.00004019567,0.002390837,0.00002249862,0.0000564863,4.000605e-7,0.09077296,0.00065533,0.000001483546,0.7574386,0.0372666,0.1112175],"study_design_scores_gemma":[0.000328947,0.00003932408,0.03687705,0.00002485283,0.00001481204,0.000001194674,0.04499963,0.003291213,0.00006758679,0.0258342,0.8883104,0.00021081],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3272978,0.000110912,0.0003611768,0.01032061,0.0003874718,0.0005719983,0.0002043482,0.0001097062,0.660636],"genre_scores_gemma":[0.9967674,0.00003100368,0.00002586811,0.002498154,0.00007014874,0.00004392186,0.00006994222,0.000002882118,0.0004906933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8510438,"threshold_uncertainty_score":0.9993989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009296202306279103,"score_gpt":0.2161456744351098,"score_spread":0.2068494721288307,"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."}}