{"id":"W4410382369","doi":"10.1017/dap.2025.17","title":"Missed opportunities in AI regulation: lessons from Canada’s AI and data act","year":2025,"lang":"en","type":"article","venue":"Data & Policy","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Political science; Data science; Public administration; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007642296,0.00008303973,0.0001503907,0.00008766881,0.0004564284,0.0003691636,0.001157712,0.0001178293,0.00006962743],"category_scores_gemma":[0.00213541,0.00008927779,0.000007313018,0.0002367075,0.0002561024,0.0010513,0.0006316272,0.0002233309,0.000001269839],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001357296,"about_ca_system_score_gemma":0.01086482,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9943066,"about_ca_topic_score_gemma":0.9936968,"domain_scores_codex":[0.9988524,0.0001633893,0.0001672783,0.0002918361,0.0002619996,0.000263076],"domain_scores_gemma":[0.9984903,0.0002860229,0.00004896035,0.0009577348,0.00007337033,0.000143633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007319361,0.0000223744,0.002171226,0.00001049848,0.00003149352,0.00001126252,0.006067958,5.533088e-7,0.00002140639,0.3598296,0.6059538,0.02587246],"study_design_scores_gemma":[0.0001689448,0.000002779358,0.02957715,0.00004578174,0.00001398403,7.487194e-8,0.004795357,0.0002135358,0.000005403276,0.03265958,0.9324005,0.0001168746],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01550513,0.0002753181,0.00001769921,0.9337518,0.0001826175,0.0001236993,0.006967504,0.00002475679,0.04315145],"genre_scores_gemma":[0.9556839,0.0008701148,0.00009056649,0.03567546,0.000527575,0.000001471895,0.004213751,0.000007525705,0.00292956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9401789,"threshold_uncertainty_score":0.9947427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3098048420941256,"score_gpt":0.4884507837526386,"score_spread":0.1786459416585131,"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."}}