{"id":"W4411718321","doi":"10.31235/osf.io/za8v6_v1","title":"Preventing and Eliminating Bots and Participant Fraud in Online Surveys: Two Case Studies from International LGBTQ+ Social Research","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Ontario; Social Sciences and Humanities Research Council of Canada; Wilfrid Laurier University","keywords":"Internet privacy; Social media; World Wide Web; Business; Computer science; Data science; Sociology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004471346,0.0001505972,0.0002784843,0.0002961169,0.0002592539,0.0003969166,0.0003587635,0.0001197034,0.000005891678],"category_scores_gemma":[0.0009808346,0.0001466502,0.00003163743,0.0002215019,0.00008437159,0.0002053296,0.003762906,0.0007692559,6.350618e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008967936,"about_ca_system_score_gemma":0.00008912556,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007034563,"about_ca_topic_score_gemma":0.01048831,"domain_scores_codex":[0.9974564,0.0009016722,0.0003810429,0.0006604762,0.0003411029,0.0002592775],"domain_scores_gemma":[0.9981914,0.001197039,0.0001152969,0.0002054906,0.000248416,0.0000424208],"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":[0.00003604308,0.0004192226,0.05731286,0.000541072,0.0006726869,0.001384546,0.06958112,0.0007164976,0.0002653522,0.003532568,0.0009189557,0.8646191],"study_design_scores_gemma":[0.001821159,0.0001120722,0.1592307,0.001684138,0.000070562,0.0001196003,0.01058873,0.770139,0.0005987849,0.05458136,0.0002184494,0.0008354301],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854783,0.0009209019,0.01077205,0.001360233,0.0009075896,0.00020341,0.00003427277,0.00006497517,0.0002582904],"genre_scores_gemma":[0.98664,0.000169799,0.01250571,0.00004287978,0.0003111789,0.00003437493,0.00001341085,0.000005144626,0.000277528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8637837,"threshold_uncertainty_score":0.9995777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3350894396779188,"score_gpt":0.4911604149140099,"score_spread":0.1560709752360911,"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."}}