{"id":"W2501318072","doi":"10.1108/s2050-206020150000009005","title":"How Far can Scholarly Networks Go? Examining the Relationships between Distance, Disciplines, Motivations, and Clusters","year":2015,"lang":"en","type":"book-chapter","venue":"","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Discipline; Excellence; Multidisciplinary approach; Geographical distance; Data science; Scholarly communication; Sociology; Public relations; Political science; Computer science; Social science; Publishing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001600969,0.0002157797,0.0002100315,0.0001106359,0.001860351,0.001503874,0.0003131586,0.0004196027,0.00005120201],"category_scores_gemma":[0.0006525778,0.0001675438,0.00003440152,0.0002249443,0.0004448064,0.0006717948,0.00007709005,0.0007248668,0.000007986802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003160839,"about_ca_system_score_gemma":0.0005926577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001512483,"about_ca_topic_score_gemma":0.01319619,"domain_scores_codex":[0.9983513,0.0002302954,0.0002847751,0.0003388125,0.0005717552,0.0002230792],"domain_scores_gemma":[0.9981304,0.0004876857,0.0003011009,0.000324495,0.0005614171,0.0001948359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002716188,0.000004726575,0.01212084,0.000004414635,0.00003583339,2.541198e-7,0.007128442,0.00004267476,7.456379e-8,0.9512646,0.02117642,0.00821899],"study_design_scores_gemma":[0.0001125684,0.00001315971,0.0104902,0.00005429659,0.00007095747,2.342216e-7,0.01517437,0.0000649348,5.27069e-8,0.02412176,0.9496238,0.0002736222],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0004189879,0.001742197,0.01210987,0.0396276,0.0009986919,0.0008836659,0.00006474959,0.0001528226,0.9440014],"genre_scores_gemma":[0.2461836,0.0003169302,0.0003351472,0.0001487229,0.001565379,0.00002778079,0.0002124611,0.00002988426,0.7511801],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9284474,"threshold_uncertainty_score":0.9995326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1089216883924259,"score_gpt":0.310552047049597,"score_spread":0.2016303586571712,"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."}}