{"id":"W3163108587","doi":"10.1145/3411764.3445359","title":"What Happens After Death? Using a Design Workbook to Understand User Expectations for Preparing their Data","year":2021,"lang":"en","type":"article","venue":"","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workbook; Key (lock); Ideation; Computer science; Everyday life; Representation (politics); External Data Representation; World Wide Web; Human–computer interaction; Multimedia; Psychology; Artificial intelligence; Computer security; Cognitive science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02925796,0.002094878,0.0006068254,0.001877971,0.004161626,0.007586229,0.002507544,0.002461617,0.003326171],"category_scores_gemma":[0.03950227,0.001045409,0.000806791,0.0007419219,0.008373518,0.009610019,0.005143399,0.003627525,0.0009068254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003654323,"about_ca_system_score_gemma":0.002734173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009560725,"about_ca_topic_score_gemma":0.001215463,"domain_scores_codex":[0.984663,0.01280475,0.0004834355,0.0007295065,0.0007234581,0.0005958501],"domain_scores_gemma":[0.9406162,0.05093056,0.001808497,0.002386069,0.002053477,0.002205204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002368939,0.0003954572,0.00590608,0.0005777407,0.0000142416,0.0008378433,0.9436964,0.0008441779,0.00702744,0.01510221,0.002791624,0.02256985],"study_design_scores_gemma":[0.0002853446,0.001988472,0.004751553,0.0007907285,0.00006580103,0.001577253,0.8582468,0.007434384,0.00841369,0.01469052,0.1014602,0.000295193],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8051187,0.0004025958,0.1713467,0.00538433,0.0001796566,0.00225011,0.0002249986,0.0008752445,0.01421769],"genre_scores_gemma":[0.8356793,0.0004661404,0.1516111,0.0009332428,0.00002850863,0.002594008,0.0002469298,0.0002926656,0.008148041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02925796,"threshold_uncertainty_score":0.1547327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2292929300066736,"score_gpt":0.3704649117365462,"score_spread":0.1411719817298726,"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."}}