{"id":"W4234844921","doi":"10.31234/osf.io/uq45c","title":"Common Concerns with MTurk as a Participant Pool: Evidence and Solutions","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Table (database); Participant observation; Data science; Psychology; Computer science; Sociology; Data mining; Social science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007607436,0.0001936485,0.0002481311,0.00004964118,0.00007324039,0.00008100904,0.0001863584,0.0002107247,0.0001141232],"category_scores_gemma":[0.00003853656,0.0001484549,0.00002998728,0.00005233188,0.0002508463,0.00006315883,0.0004790741,0.0003382234,0.00003596672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003683212,"about_ca_system_score_gemma":0.00003077228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007199886,"about_ca_topic_score_gemma":0.0008270032,"domain_scores_codex":[0.9992342,0.000008882956,0.0001502591,0.0002130986,0.0001030678,0.0002904841],"domain_scores_gemma":[0.9993736,0.00008387399,0.0000282021,0.0004266428,0.00003535578,0.00005230237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003364737,0.0003539693,0.1297826,0.008165892,0.006550876,0.0005351506,0.01649267,0.1633112,0.001994048,0.2624738,0.3613553,0.04864789],"study_design_scores_gemma":[0.002702124,0.003398447,0.1010066,0.01796113,0.002788101,0.0005530087,0.01908694,0.5265641,0.03951047,0.1447135,0.1322877,0.009427803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814955,0.00452636,0.003847865,0.0007749694,0.0004064552,0.0003368141,0.00002123177,0.002152996,0.006437786],"genre_scores_gemma":[0.997259,0.001265275,0.001021825,0.0000446554,0.00009471212,0.000101814,0.000004008768,0.00002474601,0.0001839226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3632529,"threshold_uncertainty_score":0.6053814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1166246930471828,"score_gpt":0.2936991547604612,"score_spread":0.1770744617132783,"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."}}