{"id":"W4384160076","doi":"10.2139/ssrn.4509404","title":"Favor Exchange: An Experiment","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Law, Economics, and Judicial Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; Université du Québec à Montréal","funders":"","keywords":"Econometrics; Economics; Computer 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":[],"consensus_categories":[],"category_scores_codex":[0.01032161,0.001222207,0.001898348,0.0006411252,0.003034439,0.00437148,0.002428713,0.007305682,0.06547225],"category_scores_gemma":[0.0339714,0.001060767,0.000744953,0.0006179823,0.002899362,0.005540445,0.002457808,0.006951477,0.01006578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006655945,"about_ca_system_score_gemma":0.001565558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009957203,"about_ca_topic_score_gemma":0.0007755063,"domain_scores_codex":[0.9957969,0.001871794,0.000311135,0.0008286045,0.0007072096,0.0004843345],"domain_scores_gemma":[0.9424902,0.04027185,0.004336763,0.007644573,0.001118869,0.00413777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"nonrandomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.2900433,0.3553384,0.03254849,0.002574969,0.001379074,0.00217951,0.007762549,0.005649081,0.03889098,0.1109115,0.061455,0.09126704],"study_design_scores_gemma":[0.2883171,0.1507409,0.07311233,0.0004659601,0.002753968,0.002684497,0.00609338,0.04808642,0.01927582,0.312848,0.09473423,0.0008874107],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546939,0.0003682348,0.003298819,0.003650678,0.001079205,0.001828621,0.001283014,0.0001977852,0.03359978],"genre_scores_gemma":[0.9647934,0.0002256394,0.003683609,0.003134012,0.0008273973,0.001834933,0.0009701604,0.00009742333,0.02443339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06547225,"threshold_uncertainty_score":0.2190265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04838984997280017,"score_gpt":0.2597426464800124,"score_spread":0.2113527965072123,"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."}}