{"id":"W4388332620","doi":"10.1609/hcomp.v11i1.27544","title":"Informing Users about Data Imputation: Exploring the Design Space for Dealing With Non-Responses","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Human Computation and Crowdsourcing","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto","keywords":"Imputation (statistics); Computer science; Software deployment; Autonomy; Missing data; Information retrieval; Data science; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009810514,0.0001801474,0.0001731064,0.0002479744,0.0009491003,0.000441091,0.001095379,0.00004473283,0.000001935658],"category_scores_gemma":[0.0002069052,0.0001205078,0.00002989789,0.0005300152,0.0001635185,0.001214565,0.0004707079,0.0002300579,0.000003414181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003400448,"about_ca_system_score_gemma":0.00005479786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001471438,"about_ca_topic_score_gemma":0.000003796973,"domain_scores_codex":[0.9987622,0.00001659294,0.0003116842,0.0003883617,0.0002765564,0.0002446077],"domain_scores_gemma":[0.9983287,0.0003902926,0.0004391963,0.0002574474,0.0005566723,0.0000277066],"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":[0.0002231766,0.00003382747,0.001535408,0.0002272429,0.0001301757,0.000001671826,0.01851594,0.009756989,0.02847878,0.8793572,0.0008913186,0.06084833],"study_design_scores_gemma":[0.0008913339,0.0004121855,0.01106735,0.0007717104,0.00003811955,0.00003092286,0.00398624,0.922776,0.04044139,0.01897033,0.000259807,0.0003545373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5819531,0.000005930747,0.4135877,0.003069278,0.000136112,0.0005725428,0.000002424449,0.0002157318,0.000457199],"genre_scores_gemma":[0.9817005,0.000006406209,0.01789543,0.0001440547,0.00004013064,0.00007251481,0.000003704663,0.00001637213,0.000120931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9130191,"threshold_uncertainty_score":0.7299808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2356427033280684,"score_gpt":0.3595694917627186,"score_spread":0.1239267884346501,"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."}}