{"id":"W4388821770","doi":"10.3758/s13428-023-02246-7","title":"Model-agnostic unsupervised detection of bots in a Likert-type questionnaire","year":2023,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Respondent; Computer science; Likert scale; Permutation (music); Calibration; Sensitivity (control systems); Statistical hypothesis testing; Type I and type II errors; Null hypothesis; Outlier; Relation (database); Artificial intelligence; Data mining; Machine learning; Statistics; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.01454515,0.0004575824,0.0005967006,0.0007037249,0.0004093621,0.000913421,0.001124292,0.001057831,0.002241825],"category_scores_gemma":[0.05279647,0.0003399886,0.0005920451,0.0005702274,0.000500661,0.00113105,0.0008346754,0.00101804,0.001594853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005942913,"about_ca_system_score_gemma":0.0008415278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001229708,"about_ca_topic_score_gemma":0.002053874,"domain_scores_codex":[0.9874916,0.007991693,0.0006649343,0.00163874,0.001651796,0.0005611881],"domain_scores_gemma":[0.9478978,0.03547016,0.003634871,0.006946637,0.005329301,0.0007211802],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002286132,0.003758688,0.6993363,0.0008428314,0.0004980352,0.0002227595,0.00602674,0.02422279,0.03328557,0.007166794,0.01092365,0.2114298],"study_design_scores_gemma":[0.0001627144,0.002366505,0.5064428,0.0001706324,0.0002430325,0.0004332369,0.002251159,0.4544201,0.0195056,0.00680122,0.007058761,0.0001441724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8175158,0.00004149603,0.1749654,0.0001663119,0.00006713127,0.001877789,0.001340538,0.0006759753,0.003349542],"genre_scores_gemma":[0.9554225,0.00002023704,0.0401869,0.0001477711,0.00001407606,0.001360012,0.001173793,0.00004899788,0.001625784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9854549,"threshold_uncertainty_score":0.07692307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6771024707565433,"score_gpt":0.6449932992528221,"score_spread":0.03210917150372117,"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."}}