{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.6326632,0.001949683,0.00335822,0.005219215,0.01352617,0.01454217,0.01022413,0.009976216,0.03078919],"category_scores_gemma":[0.8114596,0.003134734,0.003185129,0.007728046,0.01821141,0.02615237,0.01646728,0.008286766,0.006577095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01287727,"about_ca_system_score_gemma":0.03543757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01305599,"about_ca_topic_score_gemma":0.02630332,"domain_scores_codex":[0.2386474,0.6282059,0.05506669,0.01856794,0.05327731,0.006234752],"domain_scores_gemma":[0.134854,0.7342795,0.03615527,0.03627581,0.05164961,0.006785834],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004068526,0.001071266,0.05519364,0.06323238,0.001790305,0.001604235,0.2497259,0.001199687,0.001408865,0.1324149,0.1584322,0.329858],"study_design_scores_gemma":[0.003332051,0.003140278,0.03024594,0.1302815,0.00149351,0.002290789,0.2004146,0.00344802,0.003076992,0.150525,0.4710032,0.0007481769],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1002049,0.05824794,0.2029427,0.4830544,0.01829288,0.0699574,0.007273067,0.001237923,0.05878891],"genre_scores_gemma":[0.3731328,0.01085627,0.1655479,0.1577941,0.002953219,0.2768447,0.002473572,0.001052617,0.009344712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3673368,"threshold_uncertainty_score":0.4529917,"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."}}