{"id":"W3089857897","doi":"","title":"Co-regulation or Capitulation ? Addressing conflicts arising by AI and standardization","year":2020,"lang":"en","type":"article","venue":"Lex Electronica","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Standardization; Competition (biology); Technical standard; Computer science; Political science; Management science; Economics; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004771982,0.00008188303,0.0001270073,0.00002192966,0.0008477079,0.0003905799,0.00007092173,0.0001631439,0.0001179472],"category_scores_gemma":[0.0006144675,0.00008074738,0.00002311106,0.000190751,0.0001629653,0.0005507303,0.00000915598,0.000230623,0.000004640288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001403048,"about_ca_system_score_gemma":0.0004185392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005456849,"about_ca_topic_score_gemma":0.0006863022,"domain_scores_codex":[0.9988428,0.0001651152,0.0001535524,0.0001828601,0.0003603857,0.0002953018],"domain_scores_gemma":[0.9994365,0.00009859468,0.00009908588,0.00005561623,0.0001583779,0.0001518446],"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":[0.0006852061,0.0001213402,0.004626275,0.0001375331,0.0001801041,0.00000514548,0.2297173,0.0009851614,0.1585376,0.4164531,0.1109647,0.07758646],"study_design_scores_gemma":[0.001430506,0.0004984441,0.002765427,0.00008329569,0.00007449166,9.991838e-7,0.002118158,0.005457781,0.01266159,0.02666409,0.9476067,0.0006385284],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.38661,0.003638406,0.1280424,0.3646637,0.0006188483,0.0018781,0.00008319868,0.0008949952,0.1135703],"genre_scores_gemma":[0.9958416,0.0002744832,0.000129106,0.002960493,0.0004474518,0.000002029705,0.00003788874,0.00001259078,0.0002943505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.836642,"threshold_uncertainty_score":0.651997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07678789343127133,"score_gpt":0.4111113161157885,"score_spread":0.3343234226845171,"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."}}