{"id":"W4205730035","doi":"10.1007/978-3-030-93956-4_5","title":"Granularity and Usability in Authorization Policies","year":2022,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Usability; Principal (computer security); Set (abstract data type); Syntax; Authorization; World Wide Web; Context (archaeology); Resource (disambiguation); Computer security; Human–computer interaction; Programming language; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003006458,0.0001083391,0.0001553999,0.0006949369,0.001345538,0.0003537849,0.00123921,0.0001110229,0.00003704668],"category_scores_gemma":[0.0003637107,0.0001248891,0.00001832573,0.0004790131,0.001757276,0.005382008,0.002163156,0.0004453067,0.000004705611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003109056,"about_ca_system_score_gemma":0.000311656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002380035,"about_ca_topic_score_gemma":0.001811533,"domain_scores_codex":[0.9986705,0.0001281896,0.0004340727,0.0001889774,0.0004048327,0.0001734438],"domain_scores_gemma":[0.9986091,0.0001667828,0.0001808894,0.0008273748,0.0001432135,0.00007259537],"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":[0.000002406086,0.00001337314,0.001243873,0.00001595068,7.983478e-7,5.606724e-8,0.01333971,0.00001231633,5.077364e-7,0.9261538,0.00004701826,0.05917015],"study_design_scores_gemma":[0.0003298713,0.00004920502,0.03339164,0.00008764911,0.000006039405,0.000004166797,0.0008409739,0.02070028,0.000001504893,0.149922,0.7943069,0.0003597868],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.005805655,0.001408,0.02635264,0.01457641,0.001023617,0.002439774,0.0001729097,0.0002196782,0.9480013],"genre_scores_gemma":[0.9577835,0.02615074,0.0137028,0.00104987,0.0001168462,0.0001096398,0.0003259598,0.00001253287,0.0007480895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9519778,"threshold_uncertainty_score":0.9999546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06095335389733772,"score_gpt":0.3442671777863834,"score_spread":0.2833138238890457,"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."}}