{"id":"W4396542930","doi":"10.1109/ihtc58960.2023.10508869","title":"Improvising Age Verification Technologies in Canada: Technical, Regulatory and Social Dynamics","year":2023,"lang":"en","type":"article","venue":"","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Improvisation; Dynamics (music); Computer science; Sociology; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001310984,0.00006137865,0.00009097792,0.0001579171,0.00008585263,0.00001685272,0.00007686062,0.00009799335,0.0001968083],"category_scores_gemma":[0.00002545152,0.00006177415,0.00001371448,0.0002300514,0.00005460199,0.00005205624,0.00003304278,0.0001477218,0.00005247151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004212435,"about_ca_system_score_gemma":0.00009112823,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1271331,"about_ca_topic_score_gemma":0.7887145,"domain_scores_codex":[0.9993784,0.00002803384,0.0002016306,0.000169965,0.00008169226,0.0001402362],"domain_scores_gemma":[0.9997402,0.00004718381,0.00005092744,0.0001328741,0.00001726477,0.00001150562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00007342344,0.00008914612,0.01702316,0.0000598617,0.00004710187,0.0001170078,0.003632146,0.00002242068,0.007854856,0.4678127,0.08764535,0.4156228],"study_design_scores_gemma":[0.0004221557,0.00001724729,0.9520727,0.000009347968,0.000004558616,0.00001220009,0.03000856,0.004368187,0.0001404229,0.001286462,0.01145721,0.000200913],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.95513,0.00001439568,0.0003544126,0.005521858,0.0004609089,0.0001858526,0.00001077554,0.001073268,0.03724846],"genre_scores_gemma":[0.9978728,0.000004311408,0.00007517313,0.00009604198,0.000012137,0.00002827232,0.00002751322,0.000008012832,0.001875753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9350496,"threshold_uncertainty_score":0.8786795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02307054396194707,"score_gpt":0.3183523411356857,"score_spread":0.2952817971737386,"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."}}