{"id":"W3122874574","doi":"10.1002/asi.21326","title":"Recognizing contributions in wikis: Authorship categories, algorithms, and visualizations","year":2010,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Variety (cybernetics); Data science; Visualization; Perception; Government (linguistics); Collaborative writing; Information retrieval; World Wide Web; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01122592,0.0008024402,0.0005368011,0.008547328,0.0008016385,0.005834875,0.0006284525,0.0007479357,0.001174993],"category_scores_gemma":[0.0909692,0.0003419267,0.0003488366,0.003847513,0.0006742551,0.003773271,0.002019115,0.001004111,0.0003804532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005901752,"about_ca_system_score_gemma":0.0007328336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001527758,"about_ca_topic_score_gemma":0.001552768,"domain_scores_codex":[0.9927284,0.004072909,0.0007303748,0.0006076745,0.001674625,0.0001860613],"domain_scores_gemma":[0.891721,0.08188689,0.009688514,0.005262297,0.009990958,0.001450433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001106722,0.0004567497,0.1722144,0.001340433,0.0002902499,0.0003959693,0.03211252,0.02538073,0.02188716,0.01590615,0.01174294,0.7171659],"study_design_scores_gemma":[0.0001534288,0.0005223731,0.1238135,0.0007985668,0.0002765477,0.001141387,0.01315683,0.7356249,0.04710069,0.04538697,0.03163766,0.0003871197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7033553,0.0008613633,0.278345,0.0009703413,0.0002411034,0.0003691804,0.00111116,0.00793956,0.006807014],"genre_scores_gemma":[0.8000504,0.0002043935,0.1980597,0.00002889678,0.00005667592,0.0001224668,0.0004644135,0.0002226114,0.000790588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9941651,"threshold_uncertainty_score":0.05936909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447468859142353,"score_gpt":0.3592050349109544,"score_spread":0.3447303463195309,"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."}}