{"id":"W3207066014","doi":"","title":"See Something, Say Something: Coordinating the Disclosure of Security Vulnerabilities in Canada","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Vulnerability (computing); Full disclosure; Business; Context (archaeology); Exploit; Government (linguistics); Harm; Computer security; Legislation; Internet privacy; Public relations; Political science; Law; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.01631124,0.0003772229,0.0004570531,0.003033702,0.03404256,0.01453231,0.004535752,0.004640517,0.003439233],"category_scores_gemma":[0.0474804,0.000797159,0.0006386781,0.005796332,0.007761741,0.004323674,0.01011961,0.005616179,0.0003932178],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.2556734,"about_ca_system_score_gemma":0.5885851,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9951478,"about_ca_topic_score_gemma":0.9974577,"domain_scores_codex":[0.9726412,0.004487731,0.001146785,0.001890795,0.01210703,0.007726402],"domain_scores_gemma":[0.93497,0.01220075,0.004037537,0.002388118,0.02714579,0.01925771],"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":[0.0005111264,0.0006485572,0.1338108,0.001033303,0.0003165328,0.01106275,0.1395495,0.006661369,0.004508195,0.2116272,0.2560003,0.2342704],"study_design_scores_gemma":[0.000157785,0.0002278341,0.07950763,0.001096016,0.0002282895,0.001394027,0.1627456,0.00620247,0.003275557,0.02210498,0.722359,0.000700686],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4718649,0.009347372,0.009032729,0.3345979,0.00115589,0.001368765,0.001566749,0.0004725473,0.1705931],"genre_scores_gemma":[0.8867509,0.006030092,0.01031882,0.03897739,0.0001048417,0.0002019985,0.0007800951,0.00011808,0.05671772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7443266,"threshold_uncertainty_score":0.8633134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007724906657279681,"score_gpt":0.2269266651385803,"score_spread":0.2192017584813006,"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."}}