{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001796641,0.0003843235,0.0005222454,0.001214519,0.0002316858,0.0004976777,0.000308218,0.000166002,0.001538387],"category_scores_gemma":[0.0004268959,0.0004155699,0.0001429608,0.003259293,0.00003495587,0.00053393,0.0002157912,0.0003718738,0.0001892737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003539744,"about_ca_system_score_gemma":0.000247689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002695948,"about_ca_topic_score_gemma":0.00008430281,"domain_scores_codex":[0.9977519,0.0002813886,0.0007694752,0.0004684955,0.000228584,0.000500138],"domain_scores_gemma":[0.9982641,0.0006120782,0.0001092736,0.0005227699,0.0001560768,0.0003356681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003069323,0.0003356676,0.004948958,0.001071069,0.008984365,0.00001011031,0.01538535,0.4599117,0.01610773,0.01123717,0.007841236,0.4738597],"study_design_scores_gemma":[0.0009045753,0.0001556166,0.01753251,0.0007601121,0.006048768,0.0000209335,0.004360734,0.7772038,0.03702464,0.0006438935,0.1538016,0.001542822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003705296,0.001695992,0.9608681,0.0007216085,0.0007968784,0.000780502,0.000001981604,0.0001097788,0.03131982],"genre_scores_gemma":[0.7328092,0.0005665146,0.2590571,0.000879363,0.00005184006,0.00003416936,0.000003688891,0.00003083703,0.006567254],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7291039,"threshold_uncertainty_score":0.9998296,"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."}}