{"id":"W3166607097","doi":"","title":"On augmenting the references section with a citation network visualization","year":2021,"lang":"en","type":"article","venue":"London School of Economics and Political Science Research Online (London School of Economics and Political Science)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Complement (music); Workflow; Section (typography); Citation; Set (abstract data type); Data science; Visualization; Reading (process); Graph; Graph drawing; Information retrieval; World Wide Web; Theoretical computer science; Data mining; Programming language; Database","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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.006378257,0.0002362104,0.000435117,0.0005879806,0.0009226631,0.001341537,0.001205143,0.0001086473,0.00005890413],"category_scores_gemma":[0.002948521,0.0001771718,0.00006959806,0.001994343,0.003545093,0.001779086,0.0008257922,0.0004395913,0.00001155196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004026423,"about_ca_system_score_gemma":0.003256863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000569214,"about_ca_topic_score_gemma":0.0003688703,"domain_scores_codex":[0.9955571,0.0002233537,0.0008630952,0.00101401,0.0005385441,0.001803917],"domain_scores_gemma":[0.9950468,0.001055339,0.0002767313,0.0006867682,0.001129131,0.001805252],"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":[0.00002439332,0.0001071614,0.003961719,0.00002188537,0.00001198658,0.000001197124,0.00003405823,0.0007987404,0.000210017,0.9939348,0.00005853373,0.0008355841],"study_design_scores_gemma":[0.001380963,0.001273811,0.05999859,0.0002215018,0.00003426375,0.00006100409,0.001370303,0.5120085,0.006334074,0.4135024,0.003195722,0.0006189393],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852344,0.00009491087,0.002893253,0.006575352,0.0002525087,0.00026699,0.00006492423,0.00001771327,0.004599946],"genre_scores_gemma":[0.9946004,0.0008913134,0.003115,0.0009518248,0.0002675608,0.000007574194,0.00001898403,0.00001163829,0.000135686],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5804324,"threshold_uncertainty_score":0.9996952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06070575180629204,"score_gpt":0.3737598153031471,"score_spread":0.3130540634968551,"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."}}