{"id":"W4403437307","doi":"10.1007/s11192-024-05163-4","title":"Open access improves the dissemination of science: insights from Wikipedia","year":2024,"lang":"en","type":"article","venue":"Scientometrics","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council","keywords":"Open science; Computer science; World Wide Web; Data science; Scholarly communication; Information Dissemination; Library science; Information retrieval; Political science; Mathematics; Publishing","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":["metaresearch","bibliometrics","open_science"],"consensus_categories":[],"category_scores_codex":[0.0033348,0.0002642555,0.0006429845,0.00873981,0.0009166979,0.005109054,0.0005362761,0.0007256354,0.004155954],"category_scores_gemma":[0.05881674,0.0001462927,0.0004889663,0.009576082,0.001304468,0.005869171,0.002259435,0.0008033629,0.0006080038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009448679,"about_ca_system_score_gemma":0.001301823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006430902,"about_ca_topic_score_gemma":0.004291575,"domain_scores_codex":[0.9974505,0.001221066,0.0001719732,0.0003447334,0.0006269507,0.0001846966],"domain_scores_gemma":[0.9213329,0.05617081,0.01254321,0.002124477,0.005270376,0.002558284],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006492322,0.0005484128,0.6852838,0.002627566,0.0009125916,0.001727907,0.009648453,0.01928305,0.003036387,0.1054946,0.0237615,0.1470265],"study_design_scores_gemma":[0.0001346088,0.0003908672,0.6530441,0.001209526,0.0008259301,0.001183786,0.01196994,0.07984188,0.002797661,0.1789987,0.06941793,0.0001849371],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.939218,0.009327829,0.008754553,0.006549635,0.0001440444,0.00007133346,0.003211405,0.0002551311,0.03246802],"genre_scores_gemma":[0.9963535,0.001255899,0.0009310077,0.0001082873,0.0001482012,0.00001170355,0.0005030079,0.00003454929,0.0006538992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9994637,"threshold_uncertainty_score":0.01763636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05997419341040196,"score_gpt":0.4966438955081185,"score_spread":0.4366697020977166,"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."}}