{"id":"W4247896176","doi":"10.31234/osf.io/jw9tx","title":"Young Children use Supply and Demand to Infer Desirability","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Supply and demand; On demand; Sample (material); Constant (computer programming); Demand patterns; Economics; Consumer demand; Demand characteristics; Social desirability; Demand management; Microeconomics; Computer science; Psychology; Commerce; Social psychology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003774082,0.0003164734,0.0004100884,0.0001127528,0.00006359999,0.0001722165,0.000221046,0.000330026,0.002047199],"category_scores_gemma":[0.0001008646,0.0002720382,0.00008744217,0.000059011,0.00004479853,0.00004486052,0.0009029983,0.0006173034,0.0009513074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005424659,"about_ca_system_score_gemma":0.00006704942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001118129,"about_ca_topic_score_gemma":0.000105962,"domain_scores_codex":[0.9980771,0.0001314182,0.0003190383,0.00097504,0.0001599949,0.0003374077],"domain_scores_gemma":[0.9989098,0.0001124525,0.00007801517,0.0006682689,0.00005680586,0.000174599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005971669,0.00007302703,0.9882732,0.0000245407,0.0001354373,0.000003360781,0.001412888,0.00003670381,0.00002337292,0.001474658,0.005923735,0.002559384],"study_design_scores_gemma":[0.0003105099,0.00006312288,0.9941999,0.00006041176,0.00002897123,0.00002048791,0.00003226383,0.000004326682,0.0000198587,0.0002480996,0.004634517,0.0003774938],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820544,0.0001424296,0.0007748496,0.0004456379,0.0007363079,0.0009153709,0.0000392489,0.0001283024,0.01476347],"genre_scores_gemma":[0.9832827,0.0000200161,0.003073502,0.0007685543,0.0001364048,0.00003363146,0.00008194927,0.00003528623,0.01256791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005926761,"threshold_uncertainty_score":0.9999732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02891820095465796,"score_gpt":0.2932153406552064,"score_spread":0.2642971397005484,"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."}}