{"id":"W2550079071","doi":"10.3386/w15623","title":"The Behavioralist Visits the Factory: Increasing Productivity Using Simple Framing Manipulations","year":2009,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Incentive; Framing (construction); Conditionality; Sustenance; Reputation; Economics; Productivity; Marketing; Labour economics; Business; Microeconomics; Engineering; Political 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":[0.001639704,0.0005689065,0.0003987322,0.000341094,0.0004564366,0.0007273666,0.0005617381,0.0006903819,0.005696787],"category_scores_gemma":[0.005038656,0.0002797972,0.0002869898,0.0001835473,0.000996801,0.0005753497,0.0007697652,0.001006898,0.0003143933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005133963,"about_ca_system_score_gemma":0.0003900537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000483129,"about_ca_topic_score_gemma":0.0005547902,"domain_scores_codex":[0.9991222,0.0004451159,0.00003412223,0.0002013852,0.0001101511,0.00008708681],"domain_scores_gemma":[0.9956871,0.002067792,0.0010064,0.0007523616,0.00009453442,0.0003918717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01811834,0.0323347,0.05544819,0.00153809,0.0005551473,0.001016939,0.006085795,0.008322526,0.6075796,0.04353108,0.004618467,0.2208512],"study_design_scores_gemma":[0.00870354,0.07714184,0.4875926,0.0003881984,0.001501312,0.0009981409,0.005640833,0.05712944,0.2271517,0.1027696,0.03055449,0.0004282744],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904515,0.00007821721,0.00371022,0.0005197528,0.00005399775,0.00006828709,0.000055364,0.00005222119,0.005010494],"genre_scores_gemma":[0.9936296,0.00009673076,0.004413081,0.000317516,0.00004465426,0.0001690769,0.00003201642,0.00001623123,0.001281042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005696787,"threshold_uncertainty_score":0.01905763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5462732629295765,"score_gpt":0.5867537055625076,"score_spread":0.04048044263293116,"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."}}