{"id":"W3003652207","doi":"10.1177/0162243920904436","title":"Making Digital Territory: Cybersecurity, Techno-nationalism, and the Moral Boundaries of the State","year":2020,"lang":"en","type":"article","venue":"Science Technology & Human Values","topic":"Cybernetics and Technology in Society","field":"Arts and Humanities","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Technocracy; Nationalism; State (computer science); Information infrastructure; Sociology; Scholarship; Political science; Government (linguistics); Public administration; Public relations; Law; Information system; Politics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["sts"],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":["sts"],"domain":null,"study_design":"qualitative","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.0003739933,0.0001450335,0.0002032498,0.0001571288,0.002994671,0.0005605599,0.001249461,0.00008560081,0.00004834917],"category_scores_gemma":[0.000201688,0.00008603084,0.00007986621,0.0003896198,0.07533292,0.0002827674,0.0009531114,0.0003861203,0.000004055474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002933794,"about_ca_system_score_gemma":0.00009874451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004697818,"about_ca_topic_score_gemma":0.00009702965,"domain_scores_codex":[0.9987556,0.00001578739,0.0002505763,0.0003206121,0.0003811742,0.000276237],"domain_scores_gemma":[0.9992021,0.00004113265,0.0001899501,0.0003799839,0.0001626592,0.00002419237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003434627,0.00001397769,0.002479286,0.000007009183,0.000017077,7.000747e-7,0.02213085,0.000001089843,0.0002519898,0.9736682,0.0004519415,0.0009744677],"study_design_scores_gemma":[0.0004342324,0.00008646213,0.0005283516,0.00002959973,0.00002708229,0.000007508906,0.01501726,0.0002840988,0.001877853,0.9334316,0.04811691,0.0001591004],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622664,0.0003931696,0.000009540769,0.02646177,0.0002277239,0.0002788885,0.00004489828,0.0003465035,0.009971105],"genre_scores_gemma":[0.9991055,0.000008616973,0.00007513044,0.0003921613,0.00006441459,0.00001716578,0.000001018535,0.00001015574,0.0003258284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07233825,"threshold_uncertainty_score":0.9983033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03755315265282001,"score_gpt":0.2644189347230169,"score_spread":0.2268657820701969,"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."}}