{"id":"W3208397276","doi":"10.21203/rs.3.rs-1048207/v1","title":"The globalizability of temporal discounting","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; McGill University","funders":"Economic and Social Research Council","keywords":"Discounting; Economics; Econometrics; Finance","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":[],"consensus_categories":[],"category_scores_codex":[0.004640879,0.0002580475,0.0003401244,0.000650881,0.0001779783,0.001326173,0.0003674935,0.0002684015,0.004616938],"category_scores_gemma":[0.03815919,0.000157633,0.0003360387,0.0008388635,0.001082609,0.001345371,0.0007169163,0.001024564,0.000113195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004338945,"about_ca_system_score_gemma":0.0001892672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002739446,"about_ca_topic_score_gemma":0.001583865,"domain_scores_codex":[0.9987751,0.0004775948,0.0001004476,0.0003265582,0.0002619158,0.00005836446],"domain_scores_gemma":[0.9624413,0.02271807,0.00739606,0.005009806,0.001879898,0.0005549198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002835783,0.0002125743,0.7313907,0.000429274,0.0008965968,0.0004050973,0.004145232,0.01513179,0.01129578,0.09429969,0.00121456,0.137743],"study_design_scores_gemma":[0.000105037,0.0005607093,0.6869785,0.0001876967,0.000398797,0.0007310949,0.002353083,0.04092399,0.007112766,0.2562012,0.004332567,0.0001146509],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9621137,0.0005567965,0.02275554,0.0004350943,0.00004481383,0.00003055341,0.0003804016,0.00003260549,0.01365042],"genre_scores_gemma":[0.9986917,0.00007819646,0.0009310907,0.00002269639,0.00001036344,0.000004766531,0.00005930384,0.00000782367,0.0001940443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004640879,"threshold_uncertainty_score":0.02454358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3427071615659912,"score_gpt":0.5597152017425966,"score_spread":0.2170080401766054,"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."}}