{"id":"W4391903742","doi":"10.24251/hicss.2023.555","title":"Introduction to the Minitrack on Design and Appropriation of Knowledge and AI Systems","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Knowledge management; Computer science; Context (archaeology); Knowledge transfer; Presentation (obstetrics); Process (computing); Information system; Soft systems methodology; Social media; Personal knowledge management; Data science; Management information systems; Organizational learning; Engineering; World Wide Web","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":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.005598285,0.0005868744,0.0007073053,0.001173038,0.001199011,0.00122796,0.008040418,0.0002091297,0.00001395288],"category_scores_gemma":[0.000257898,0.0003529634,0.0002287233,0.002856142,0.001383375,0.002666399,0.00171587,0.000544982,0.00006241826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003432937,"about_ca_system_score_gemma":0.0003702676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002234602,"about_ca_topic_score_gemma":0.00001249398,"domain_scores_codex":[0.9927446,0.0001240746,0.001527603,0.001533975,0.003450925,0.0006187974],"domain_scores_gemma":[0.9901358,0.0005100784,0.002158276,0.0004164441,0.00657774,0.0002016935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001223782,0.0001073395,0.0009666146,0.0002434249,0.0000813306,3.379329e-7,0.004797904,0.001018545,0.001437952,0.9853309,0.005182442,0.000710771],"study_design_scores_gemma":[0.0009534842,0.002408403,0.007137346,0.007219282,0.00008731095,0.0003022972,0.9134883,0.05461337,0.005464361,0.004238111,0.003070138,0.001017535],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1094055,0.00006729864,0.001742768,0.06461675,0.01175405,0.003263139,0.000434721,0.0003823855,0.8083334],"genre_scores_gemma":[0.9970515,0.00003958878,0.0007565019,0.0001392658,0.0006112383,0.0001894897,0.000002093293,0.00002367307,0.001186617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9810929,"threshold_uncertainty_score":0.9998922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05049605989711024,"score_gpt":0.3067553592543634,"score_spread":0.2562592993572532,"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."}}