{"id":"W4313262081","doi":"10.1111/epic.12114","title":"How a Government Organisation Evolved to Embrace Ethnographic Methods for Service (and Team) Resilience: The Case of the Canadian Digital Service","year":2022,"lang":"en","type":"article","venue":"Ethnographic Praxis in Industry Conference Proceedings","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Treasury Board of Canada Secretariat; Government of Canada","funders":"","keywords":"Ethnography; Bureaucracy; Resilience (materials science); Government (linguistics); Public relations; Service (business); Sociology; Psychological resilience; Political science; Knowledge management; Business; Psychology; Marketing; Social psychology; Politics; 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":[0.02314462,0.0006900678,0.0003620162,0.003635673,0.04776349,0.01736983,0.00281794,0.004210183,0.002880068],"category_scores_gemma":[0.02895439,0.0008914859,0.0005782072,0.003555905,0.04579253,0.008691563,0.01578101,0.00560576,0.0007215766],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06065364,"about_ca_system_score_gemma":0.04413988,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6376348,"about_ca_topic_score_gemma":0.7673419,"domain_scores_codex":[0.9615389,0.02779131,0.0005087568,0.001720304,0.002921816,0.005519109],"domain_scores_gemma":[0.9746651,0.01209079,0.001135594,0.002759231,0.005015387,0.004333836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004314326,0.00006302165,0.005406843,0.00007443114,0.0000128743,0.003270034,0.9388086,0.0006282027,0.0009172307,0.0267305,0.003535145,0.02050999],"study_design_scores_gemma":[0.00001221214,0.00004878796,0.003516467,0.0002543214,0.00001635015,0.001002885,0.8723356,0.0009226747,0.0006065527,0.004779655,0.116406,0.0000985731],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8285098,0.00119139,0.02417223,0.03494283,0.0003993594,0.0006195068,0.0001145711,0.0002146058,0.1098357],"genre_scores_gemma":[0.9800465,0.0003497382,0.006708339,0.001435744,0.00002368435,0.0000988696,0.00002960919,0.0001087562,0.01119872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9393464,"threshold_uncertainty_score":0.7289985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02954047826893859,"score_gpt":0.2790944310269569,"score_spread":0.2495539527580183,"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."}}