{"id":"W3028990678","doi":"10.1145/3313831.3376636","title":"Memory through Design: Supporting Cultural Identity for Immigrants through a Paper-Based Home Drafting Tool","year":2020,"lang":"en","type":"article","venue":"","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Immigration; Artifact (error); Storytelling; Identity (music); Space (punctuation); Computer science; Frame (networking); Collaborative design; Process (computing); Sociology; Cultural heritage; Collective memory; Human–computer interaction; Narrative; Aesthetics; Systems design; History; Political science; Linguistics; Artificial intelligence; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003260403,0.0008173827,0.0002192666,0.00105721,0.002163568,0.003593232,0.001356159,0.001010982,0.007493908],"category_scores_gemma":[0.009913071,0.0002256535,0.0004618915,0.0006455397,0.003322095,0.002782854,0.004060198,0.0005754777,0.000736731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009920605,"about_ca_system_score_gemma":0.001531221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002810418,"about_ca_topic_score_gemma":0.006508671,"domain_scores_codex":[0.9988231,0.0007474246,0.00004823685,0.0001029934,0.00018035,0.00009779983],"domain_scores_gemma":[0.9957036,0.002689383,0.0002640089,0.0007623465,0.0002866725,0.000294035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004065497,0.0003601122,0.01284152,0.001127374,0.00003685868,0.002931437,0.5265402,0.001226637,0.02828111,0.02286058,0.008746097,0.3946415],"study_design_scores_gemma":[0.000300434,0.001676003,0.01694074,0.001441191,0.000234935,0.005576546,0.3711171,0.008369994,0.02743424,0.02056815,0.5460588,0.0002819204],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7784454,0.0009032668,0.146239,0.001582373,0.0002130386,0.0006796578,0.0002794567,0.002025497,0.06963221],"genre_scores_gemma":[0.8618057,0.0004030826,0.122202,0.0002075261,0.00003090546,0.0003254271,0.000149345,0.0001775627,0.01469844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007493908,"threshold_uncertainty_score":0.02506965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0882060382531488,"score_gpt":0.3457376115638301,"score_spread":0.2575315733106813,"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."}}