{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005844994,0.0004119977,0.0003511794,0.004013384,0.01541968,0.01009885,0.002380861,0.001809993,0.00609573],"category_scores_gemma":[0.01614799,0.0004368424,0.0004641314,0.004736198,0.007697362,0.002829198,0.004379652,0.003259872,0.0006504237],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.2057692,"about_ca_system_score_gemma":0.2614896,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9921094,"about_ca_topic_score_gemma":0.9947618,"domain_scores_codex":[0.9868425,0.001324242,0.000328013,0.001020279,0.007086837,0.003398202],"domain_scores_gemma":[0.9724266,0.004789503,0.002297171,0.0006842315,0.01453736,0.005265214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004658194,0.0004000653,0.1501164,0.00072197,0.00008672109,0.001731284,0.07726693,0.003923646,0.00536062,0.3321544,0.07052894,0.3572432],"study_design_scores_gemma":[0.00005458833,0.0001947461,0.2703983,0.0009674206,0.0001116994,0.0006768405,0.107928,0.009377179,0.004916004,0.01948371,0.5854325,0.0004588707],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5752329,0.01247959,0.0166948,0.09107607,0.0006579622,0.0005046732,0.001472366,0.0007447065,0.3011371],"genre_scores_gemma":[0.9528723,0.00498312,0.004120019,0.003143222,0.00005824035,0.00005623431,0.0003974476,0.0001047843,0.03426461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9845803,"threshold_uncertainty_score":0.9211952,"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."}}