Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Bjorkegren, Dan & Blumenstock, Joshua & Knight, Samsun, 2022.
"(Machine) Learning What Policies Value,"
CEPR Discussion Papers
17364, C.E.P.R. Discussion Papers.
- Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2022. "(Machine) Learning What Policies Value," Papers 2206.00727, arXiv.org.
- Harjit Singh & Avneet Singh, 2023. "ChatGPT: Systematic Review, Applications, and Agenda for Multidisciplinary Research," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(2), pages 193-212, April.
- Drew Fudenberg & Annie Liang, 2019.
"Predicting and Understanding Initial Play,"
American Economic Review, American Economic Association, vol. 109(12), pages 4112-4141, December.
- Drew Fudenberg & Annie Liang, 2017. "Predicting and Understanding Initial Play," PIER Working Paper Archive 17-026, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 04 Jan 2018.
- Drew Fudenberg & Annie Liang, 2017. "Predicting and Understanding Initial Play," PIER Working Paper Archive 18-009, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 30 Apr 2018.
- Anja Lambrecht & Catherine Tucker, 2019. "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, INFORMS, vol. 65(7), pages 2966-2981, July.
- John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers 31122, National Bureau of Economic Research, Inc.
- Gary Charness & Brian Jabarian & John A. List, 2023.
"Generation Next: Experimentation with AI,"
NBER Working Papers
31679, National Bureau of Economic Research, Inc.
- Gary Charness & Brian Jabarian & John List, 2023. "Generation Next: Experimentation with AI," Artefactual Field Experiments 00777, The Field Experiments Website.
- David Dranove & Ginger Zhe Jin, 2010.
"Quality Disclosure and Certification: Theory and Practice,"
Journal of Economic Literature, American Economic Association, vol. 48(4), pages 935-963, December.
- David Dranove & Ginger Zhe Jin, 2010. "Quality Disclosure and Certification: Theory and Practice," NBER Working Papers 15644, National Bureau of Economic Research, Inc.
- Ginger Zhe Jin & Michael Luca & Daniel Martin, 2021.
"Is No News (Perceived As) Bad News? An Experimental Investigation of Information Disclosure,"
American Economic Journal: Microeconomics, American Economic Association, vol. 13(2), pages 141-173, May.
- Ginger Zhe Jin & Michael Luca & Daniel Martin, 2015. "Is No News (Perceived as) Bad News? An Experimental Investigation of Information Disclosure," NBER Working Papers 21099, National Bureau of Economic Research, Inc.
- Korinek, Anton, 2023.
"Language Models and Cognitive Automation for Economic Research,"
CEPR Discussion Papers
17923, C.E.P.R. Discussion Papers.
- Anton Korinek, 2023. "Language Models and Cognitive Automation for Economic Research," NBER Working Papers 30957, National Bureau of Economic Research, Inc.
- Athey, Susan & Imbens, Guido W., 2019.
"Machine Learning Methods Economists Should Know About,"
Research Papers
3776, Stanford University, Graduate School of Business.
- Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
- Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1.
- Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
- Chen-Fu Chien & Stéphane Dauzère-Pérès & Woonghee Tim Huh & Young Jae Jang & James R. Morrison, 2020. "Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2730-2731, May.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022.
"Urban economics in a historical perspective: Recovering data with machine learning,"
Regional Science and Urban Economics, Elsevier, vol. 94(C).
- Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020. "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers 15308, C.E.P.R. Discussion Papers.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," PSE-Ecole d'économie de Paris (Postprint) halshs-03673240, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print halshs-03673240, HAL.
- Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," SciencePo Working papers Main halshs-03673240, HAL.
- Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021.
"China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach,"
American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
- Shao, Yongtong & Xiong, Tao & Li, Minghao & Hayes, Dermot & Zhang, Wendong & Xie, Wei, 2020. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," ISU General Staff Papers 202001010800001619, Iowa State University, Department of Economics.
- Yongtong Shao & Minghao Li & Dermot J. Hayes & Wendong Zhang & Tao Xiong & Wei Xie, 2020. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," Center for Agricultural and Rural Development (CARD) Publications 20-wp607, Center for Agricultural and Rural Development (CARD) at Iowa State University.
- Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023.
"Big data forecasting of South African inflation,"
Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
- Byron Botha & Kevin Kotze & Neil Rankin & Rulof P. Burger, 2022. "Big data forecasting of South African inflation," Working Papers 873, Economic Research Southern Africa.
- Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
- Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
- Felix Chopra & Ingar Haaland, 2023.
"Conducting qualitative interviews with AI,"
CEBI working paper series
23-06, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
- Felix Chopra & Ingar Haaland & Ingar K. Haaland, 2023. "Conducting Qualitative Interviews with AI," CESifo Working Paper Series 10666, CESifo.
- Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies," MPRA Paper 109137, University Library of Munich, Germany.
- Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
- Feras A. Batarseh & Munisamy Gopinath & Anderson Monken & Zhengrong Gu, 2021. "Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning," Papers 2111.07508, arXiv.org.
- Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
- Ay, Jean-Sauveur & Le Gallo, Julie, 2021.
"The Signaling Values of Nested Wine Names,"
Working Papers
321851, American Association of Wine Economists.
- Jean-Sauveur Ay & Julie Le Gallo, 2021. "The signaling value of nested wine names," Post-Print hal-03268014, HAL.
- Arenas, Andreu & Calsamiglia, Caterina, 2022.
"Gender Differences in High-Stakes Performance and College Admission Policies,"
IZA Discussion Papers
15550, Institute of Labor Economics (IZA).
- Andreu Arenas & Caterina Calsamiglia, 2023. "Gender Differences in High-Stakes Performance and College Admission Policies," Working Papers 2023/13, Institut d'Economia de Barcelona (IEB).
- Tsang, Andrew, 2021.
"Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy,"
MPRA Paper
110703, University Library of Munich, Germany.
- Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," WiSo-HH Working Paper Series 62, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
- Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
- Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021.
"Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements,"
Policy Research Working Paper Series
9838, The World Bank.
- Dang, Hai-Anh H & Kilic, Talip & Abanokova, Kseniya & Carletto, Calogero, 2023. "Poverty Imputation in Contexts without Consumption Data: A Revisit with Further Refinements," IZA Discussion Papers 15873, Institute of Labor Economics (IZA).
- Dang, Hai-Anh H. & Kilic, Talip & Abanokova, Kseniya & Carletto, Calogero, 2023. "Poverty Imputation in Contexts without Consumption Data: A Revisit with Further Refinements," GLO Discussion Paper Series 1226, Global Labor Organization (GLO).
- Dang, Hai-Anh H. & Kilic, Talip & Abanokova, Kseniya & Carletto, Calogero, 2024. "Poverty imputation in contexts without consumption data: a revisit with further refinements," LSE Research Online Documents on Economics 125798, London School of Economics and Political Science, LSE Library.
- Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
- Blankenship, Brian & Aklin, Michaël & Urpelainen, Johannes & Nandan, Vagisha, 2022. "Jobs for a just transition: Evidence on coal job preferences from India," Energy Policy, Elsevier, vol. 165(C).
- Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
- Donna B. Gilleskie, 2021. "In sickness and in health, until death do us part: A case for theory," Southern Economic Journal, John Wiley & Sons, vol. 87(3), pages 753-768, January.
- Askitas, Nikos, 2024.
"A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code,"
IZA Discussion Papers
17014, Institute of Labor Economics (IZA).
- Nikos Askitas & Nikolaos Askitas, 2024. "A Hands-On Machine Learning Primer for Social Scientists: Math, Algorithms and Code," CESifo Working Paper Series 11353, CESifo.
- Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-AIN-2024-01-08 (Artificial Intelligence)
- NEP-CMP-2024-01-08 (Computational Economics)
- NEP-SOG-2024-01-08 (Sociology of Economics)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2311.14720. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.