{"id":"W2189293906","doi":"10.1007/978-3-319-02717-3_6","title":"Case Study: Integrity of Wikipedia Articles","year":2013,"lang":"en","type":"book-chapter","venue":"SpringerBriefs in applied sciences and technology","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Research integrity; Crowdsourcing; Data integrity; Academic integrity; Integrity management; Structural integrity; Personal Integrity; Computer science; Data science; World Wide Web; Computer security; Engineering; Engineering ethics; Psychology; Library science; Programming language","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.005890901,0.0004921451,0.0004627628,0.003904029,0.006407845,0.00385182,0.002116368,0.004797798,0.004290061],"category_scores_gemma":[0.06387014,0.0003500002,0.0006272777,0.004266844,0.003067369,0.005549141,0.00281623,0.002127711,0.001010669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001503579,"about_ca_system_score_gemma":0.003012557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009819901,"about_ca_topic_score_gemma":0.01166885,"domain_scores_codex":[0.9894756,0.003510851,0.0009512113,0.001203232,0.00415797,0.0007010803],"domain_scores_gemma":[0.8797514,0.08505832,0.006093274,0.01143512,0.01518025,0.002481627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.002038405,0.003991546,0.1177084,0.004695452,0.0003996849,0.1377608,0.1868254,0.01073158,0.02986999,0.06972902,0.09092934,0.3453204],"study_design_scores_gemma":[0.0002554246,0.001023169,0.04843732,0.002160876,0.0006650146,0.1084759,0.1514803,0.04536239,0.138226,0.04089299,0.4625741,0.0004466421],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8763462,0.001325859,0.04910273,0.009691996,0.0005404459,0.0009645618,0.002128868,0.0009264807,0.05897289],"genre_scores_gemma":[0.9407811,0.0007464085,0.03720845,0.0006953736,0.0002098364,0.0002306852,0.001559373,0.0003656694,0.01820314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9952022,"threshold_uncertainty_score":0.03115445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03983905475490193,"score_gpt":0.3138586228040349,"score_spread":0.274019568049133,"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."}}