{"id":"W4417302724","doi":"10.31274/itaa.19776","title":"Using Precedent Analysis, Interviews, Archetypes, and Design Sprints to Inform Designing","year":2025,"lang":"","type":"article","venue":"","topic":"Design Education and Practice","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Action (physics); Work (physics); Key (lock); Process (computing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04368056,0.001320346,0.0006789688,0.007607104,0.00901927,0.006435758,0.002185393,0.00163761,0.004749021],"category_scores_gemma":[0.0676585,0.001157152,0.0006571737,0.004706107,0.008242067,0.008867281,0.004605542,0.003601814,0.001353453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0121673,"about_ca_system_score_gemma":0.01779107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008742181,"about_ca_topic_score_gemma":0.02488736,"domain_scores_codex":[0.9575804,0.0315418,0.002228577,0.003147278,0.004324516,0.001177428],"domain_scores_gemma":[0.8802474,0.09321913,0.003063817,0.01226481,0.01005364,0.001151217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002948211,0.0006324426,0.01104853,0.001297973,0.00004156145,0.0009496746,0.6235272,0.002003263,0.01483926,0.1108842,0.006011856,0.2284691],"study_design_scores_gemma":[0.0001411761,0.0005546272,0.01001156,0.003499543,0.0001012819,0.001068322,0.6146332,0.008104458,0.03200913,0.1374329,0.1922207,0.0002231177],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2843019,0.001034873,0.6152639,0.003886831,0.0002119713,0.006272112,0.0008046305,0.0004602234,0.08776366],"genre_scores_gemma":[0.5482138,0.0009230898,0.4192806,0.0009459658,0.00003237007,0.006050911,0.0009448919,0.0002379082,0.02337051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04368056,"threshold_uncertainty_score":0.2310076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1157985773930472,"score_gpt":0.3760884020804307,"score_spread":0.2602898246873835,"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."}}