{"id":"W2942880182","doi":"10.1145/3290607.3312970","title":"Worker Demographics and Earnings on Amazon Mechanical Turk","year":2019,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ministry of Education - Singapore","keywords":"Earnings; Wage; Demographics; Amazon rainforest; Residence; Work (physics); Demographic economics; Business; Labour economics; Significant difference; Economics; Demography; Engineering; Statistics; Sociology; Accounting; Mechanical engineering; Mathematics","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":[0.0002799821,0.0001074788,0.0001212428,0.00007839286,0.00008268952,0.0001624493,0.0002302982,0.00007792835,0.00002904196],"category_scores_gemma":[0.00002251114,0.00008571405,0.00004457602,0.0002413159,0.00002257473,0.0001208206,0.000147612,0.0002046403,0.0001510086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000778534,"about_ca_system_score_gemma":0.00001212698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001100481,"about_ca_topic_score_gemma":0.000002924381,"domain_scores_codex":[0.9990616,0.00004127583,0.0001121578,0.000369544,0.0001934185,0.0002220308],"domain_scores_gemma":[0.9992982,0.0001316619,0.00003640128,0.0004119352,0.00002777098,0.00009399166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004637478,0.0001553562,0.09190312,0.00004404071,0.00006491702,0.00004634689,0.001136994,0.0004033411,0.02106662,0.7009743,0.004793628,0.179365],"study_design_scores_gemma":[0.004138607,0.002609428,0.2276423,0.0008908021,0.00005269655,0.0004060896,0.0006374728,0.5909923,0.06549178,0.01295434,0.09115487,0.003029245],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9324326,0.00003174091,0.05921569,0.0007011615,0.0002582309,0.00008404822,7.766543e-8,0.0002201144,0.007056325],"genre_scores_gemma":[0.9905387,0.00000730706,0.006624764,0.0008360105,0.00003148026,0.0000015362,2.698323e-7,0.000008009205,0.001951986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6880199,"threshold_uncertainty_score":0.3495317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006023583980606697,"score_gpt":0.2002031942815284,"score_spread":0.1941796103009217,"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."}}