{"id":"W4386252546","doi":"10.31219/osf.io/7fdb5","title":"Educational Data Comics: What can Comics do for Education in Visualization?","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Austrian Science Fund","keywords":"Comics; Visualization; Computer science; Information visualization; Data visualization; World Wide Web; Data science; Subject (documents); Multimedia; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004491831,0.0002164156,0.0002467562,0.0006156461,0.00005793363,0.001347706,0.002897295,0.0001696809,0.0000295942],"category_scores_gemma":[0.0001858691,0.0002430139,0.00004080183,0.0005075419,0.00003629856,0.0006737454,0.002625836,0.0001266288,0.0000269077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001054455,"about_ca_system_score_gemma":0.002278272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007437833,"about_ca_topic_score_gemma":0.002038661,"domain_scores_codex":[0.9981362,0.00006009988,0.0004503677,0.0008845066,0.0002534128,0.0002154104],"domain_scores_gemma":[0.9972332,0.0001473209,0.0002766819,0.002088435,0.0001503961,0.0001039422],"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":[8.377647e-7,0.0001790377,0.001045133,0.0001033802,0.00001667681,1.281295e-7,0.0001669357,0.0002525567,7.686001e-7,0.5122681,0.4837144,0.002252093],"study_design_scores_gemma":[0.0003101535,0.000009549235,0.0003881658,0.0004133437,0.00001699396,0.000001564843,0.0005439439,0.8994844,0.000011265,0.0552695,0.04308966,0.0004614486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005359916,0.0001910256,0.986954,0.006591438,0.002903078,0.0007968053,0.0005989474,0.0005314659,0.001379623],"genre_scores_gemma":[0.01282356,0.006671958,0.6125112,0.01236376,0.001572858,0.0006962054,0.1650365,0.0007892963,0.1875347],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8992319,"threshold_uncertainty_score":0.999689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1027133357837961,"score_gpt":0.4238256684920536,"score_spread":0.3211123327082575,"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."}}