{"id":"W4413575767","doi":"10.51644/bap75","title":"Code Before Clause: Building Canada’s Digital Defences Before Negotiating Trade","year":2025,"lang":"en","type":"article","venue":"Balsillie Papers","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Negotiation; Code (set theory); Computer science; Business; Programming language; International trade; Political science; Computer security; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001612117,0.0001487389,0.0001813034,0.00005122395,0.000801809,0.0002431992,0.0003830036,0.0001014474,0.00007839773],"category_scores_gemma":[0.0005683625,0.0001518116,0.00007325497,0.0004241847,0.0002323124,0.0002431559,0.00003664905,0.0001639428,0.000004031099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006160385,"about_ca_system_score_gemma":0.002806385,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8923394,"about_ca_topic_score_gemma":0.9945726,"domain_scores_codex":[0.9984384,0.0000425527,0.0002278529,0.0002840759,0.0004101681,0.0005969084],"domain_scores_gemma":[0.999359,0.0001372023,0.00009677191,0.0001607996,0.00002173104,0.0002244762],"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":[0.000014877,0.0000317704,0.06715678,0.00006574037,0.0001300217,0.00006303653,0.01473462,0.0005620407,0.0001185517,0.5665006,0.2398622,0.1107597],"study_design_scores_gemma":[0.000130014,0.00001261662,0.03233988,0.00006742677,0.00001512754,8.11086e-7,0.003730717,0.00007144815,0.00001587532,0.001134643,0.9623197,0.0001617398],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4290907,0.00142293,0.00005314963,0.09433614,0.002323901,0.0006093238,0.001652375,0.0002052086,0.4703063],"genre_scores_gemma":[0.9839231,0.00003607939,0.00005217837,0.003896147,0.0002741088,0.00001095023,0.000007878006,0.00001092334,0.01178865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7224575,"threshold_uncertainty_score":0.6190699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007805416221629373,"score_gpt":0.2610254948724437,"score_spread":0.2532200786508143,"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."}}