{"id":"W4292513552","doi":"10.1093/grurint/ikac076","title":"What’s Up, Latin America? Between Competition, Data and Consumer Protection","year":2022,"lang":"en","type":"article","venue":"GRUR International","topic":"European Criminal Justice and Data Protection","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Enforcement; Latin Americans; Legislation; Position (finance); Competition (biology); Data Protection Act 1998; Business; Consumer protection; Privacy policy; Competition law; Action (physics); Law and economics; International trade; Information privacy; Political science; Economics; Law; Commerce; Market economy; Finance","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002401105,0.0002423606,0.000319943,0.001136147,0.004577161,0.006142975,0.0004873728,0.002008259,0.006403199],"category_scores_gemma":[0.003396333,0.0001458616,0.0003778001,0.002147,0.007626286,0.002877381,0.002738331,0.002491593,0.0002335837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005275003,"about_ca_system_score_gemma":0.002964307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07227163,"about_ca_topic_score_gemma":0.06694878,"domain_scores_codex":[0.9984487,0.0005843368,0.00004377827,0.0001970186,0.0001935785,0.0005325838],"domain_scores_gemma":[0.9984093,0.0007848956,0.0002807758,0.0001338304,0.0002708032,0.0001204677],"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":[0.0002133302,0.0001976249,0.05248982,0.0004294537,0.00009591871,0.006190308,0.05991958,0.0003347951,0.001907225,0.7939651,0.0212083,0.0630486],"study_design_scores_gemma":[0.00008789518,0.0001421669,0.06347414,0.001977836,0.0001407601,0.002804708,0.2256173,0.0008167152,0.00222258,0.07314344,0.6294714,0.0001011784],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.490205,0.006719581,0.001896352,0.1038992,0.0003067633,0.00006820077,0.0001638906,0.0000314579,0.3967095],"genre_scores_gemma":[0.9801175,0.0018678,0.0004458776,0.008802841,0.00008310004,0.00003271852,0.00004270536,0.00001661673,0.008590867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07227163,"threshold_uncertainty_score":0.143702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1312287830290086,"score_gpt":0.3521184238782406,"score_spread":0.220889640849232,"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."}}