{"id":"W4290547251","doi":"10.29173/cais1257","title":"Addressing transdisciplinary challenges through technology","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Interdisciplinary Research and Collaboration","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Interdisciplinary Research in Music Media and Technology; McGill University","funders":"","keywords":"Transdisciplinarity; Engineering ethics; Work (physics); Sociology; Knowledge management; Engineering; Social science; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.04553363,0.001187309,0.001165192,0.003005927,0.01333424,0.02711309,0.004909252,0.008840976,0.01597073],"category_scores_gemma":[0.04170511,0.0006484533,0.001750702,0.003007025,0.01585825,0.02831345,0.03656999,0.008921413,0.004864963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00444634,"about_ca_system_score_gemma":0.01530965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002071209,"about_ca_topic_score_gemma":0.003476104,"domain_scores_codex":[0.9498305,0.03726887,0.001579837,0.003339198,0.005106954,0.002874525],"domain_scores_gemma":[0.9340659,0.03627174,0.002149457,0.01175595,0.008316263,0.00744064],"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":[0.0001238359,0.0005695164,0.00517247,0.001561752,0.0001637468,0.001167025,0.08315442,0.00309595,0.004581711,0.6071796,0.03081668,0.2624133],"study_design_scores_gemma":[0.00003834043,0.0002132987,0.0009983326,0.001354318,0.00004858958,0.0008977948,0.08393487,0.002675994,0.001809413,0.4933594,0.4145862,0.00008337521],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06617158,0.02210984,0.441294,0.1940232,0.006233702,0.0008025297,0.0002123259,0.000766632,0.2683862],"genre_scores_gemma":[0.5996186,0.01924024,0.3054073,0.0222308,0.002090353,0.002948377,0.0003798316,0.0005299596,0.04755456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04553363,"threshold_uncertainty_score":0.2408077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1681955853562181,"score_gpt":0.3864095609114644,"score_spread":0.2182139755552462,"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."}}