{"id":"W2986862432","doi":"10.1145/3359131","title":"Makers and Quilters","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Diversification (marketing strategy); Inclusion (mineral); Context (archaeology); Space (punctuation); Sociology; Psychology; Knowledge management; Social psychology; Marketing; Business; Computer science; Geography","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":[0.00005559857,0.00008632026,0.00008564834,0.00007189225,0.00002264034,0.00003125734,0.0002633093,0.00004293058,0.00002375167],"category_scores_gemma":[0.00001433877,0.00006512177,0.00003526267,0.0000778216,0.0000178962,0.0001527634,0.00008688807,0.0001472411,0.0000224864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004274311,"about_ca_system_score_gemma":0.000001372789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004636541,"about_ca_topic_score_gemma":1.809373e-7,"domain_scores_codex":[0.9995742,0.000001076009,0.0001205399,0.0001089313,0.0001012901,0.00009390006],"domain_scores_gemma":[0.9997548,0.00002077481,0.00003486195,0.0001348017,0.00002662597,0.00002810956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007072225,0.0001823458,0.009639833,0.003447656,0.0002785629,2.977261e-7,0.004594304,0.004164197,0.7785731,0.004346905,0.1190371,0.07566494],"study_design_scores_gemma":[0.002462791,0.001061657,0.2941809,0.003470526,0.0001238107,0.0001168094,0.001114878,0.208601,0.4213873,0.009250255,0.05668008,0.001549999],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958291,0.00001215186,0.00004381249,0.0004540506,0.001677129,0.00009389511,5.154423e-7,0.0001002366,0.00178911],"genre_scores_gemma":[0.9988025,0.000005057817,0.0007814319,0.0000730441,0.0001749208,0.000005016862,9.265814e-7,0.00001387741,0.0001432359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3571858,"threshold_uncertainty_score":0.2655589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01689255924796108,"score_gpt":0.2485310042368412,"score_spread":0.2316384449888801,"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."}}