{"id":"W4391204451","doi":"10.1145/3623509.3633359","title":"Knitting Interactive Spaces: Fabricating Data Physicalizations of Local Community Visitors with Circular Knitting Machines","year":2024,"lang":"en","type":"article","venue":"","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Visualization; Computer science; Subject matter; Interpretation (philosophy); Data visualization; Human–computer interaction; World Wide Web; Multimedia; Artificial intelligence; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005418235,0.0001812543,0.000213151,0.0003194259,0.0003581232,0.0002432696,0.001572802,0.00006049033,0.00002693562],"category_scores_gemma":[0.0002162369,0.0001469537,0.00003542233,0.001520401,0.000220593,0.002042975,0.001240196,0.0008261763,0.00004413322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000919848,"about_ca_system_score_gemma":0.00007271541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004726636,"about_ca_topic_score_gemma":0.00003304202,"domain_scores_codex":[0.9985341,0.0002478179,0.0003527903,0.000421312,0.0002327692,0.0002111744],"domain_scores_gemma":[0.997394,0.0007605673,0.0002173887,0.001279401,0.0003222178,0.00002646454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002905824,0.0009074261,0.004133438,0.0007889437,0.00114321,0.00009599019,0.0220245,0.002543098,0.04822837,0.4674726,0.001646268,0.4509871],"study_design_scores_gemma":[0.0001302252,0.0001360425,0.0003799904,0.0005328775,0.00003011074,0.00008792143,0.005148526,0.9735085,0.01713988,0.001411555,0.00124624,0.000248069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06129873,0.0000368078,0.9305324,0.0004348399,0.0001742987,0.0001360114,0.000009614442,0.000561684,0.006815617],"genre_scores_gemma":[0.9647959,7.955228e-7,0.0348951,0.00007380523,0.00005803896,0.00001022062,0.00005314096,0.0000190386,0.00009389405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9709654,"threshold_uncertainty_score":0.5992597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03440888324318413,"score_gpt":0.3160144316393964,"score_spread":0.2816055483962122,"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."}}