Bibliographies thématiques / Bias

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Littérature scientifique sur le sujet « Bias »

Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 25 juillet 2025

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Articles de revues sur le sujet "Bias"

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Bailey, R. A. "Bias bias." New Scientist 199, no. 2664 (2008): 22–23. http://dx.doi.org/10.1016/s0262-4079(08)61727-3.

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Tandoc, Edson C., Bruno Takahashi, and Ryan J. Thomas. "Bias vs. Bias." Journalism Practice 12, no. 7 (2017): 834–49. http://dx.doi.org/10.1080/17512786.2017.1343095.

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Raine, Rosalind. "Bias measuring bias." Journal of Health Services Research & Policy 7, no. 1 (2002): 65–67. http://dx.doi.org/10.1258/1355819021927584.

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The British National Health Service and other publicly funded health systems operate on the principle that health care should be provided solely on the basis of need. Yet the literature abounds with reports of bias in health care use. In order to defend such a charge, two conditions must be met. The first condition is that treatment decisions must be shown to be unfair in that they are not made solely on the basis of need. This paper demonstrates the importance of considering the fair distribution of health care from two, related, perspectives. The first is that people with equal needs should
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Brighton, Henry, and Gerd Gigerenzer. "The bias bias." Journal of Business Research 68, no. 8 (2015): 1772–84. http://dx.doi.org/10.1016/j.jbusres.2015.01.061.

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Harcum, E. Rae, and Ellen Rosen. "Bias, bias, who doesn't have the bias?" Contemporary Psychology: A Journal of Reviews 40, no. 6 (1995): 607. http://dx.doi.org/10.1037/005052.

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Gibson, John P. "Bias in naming bias." Nature Reviews Genetics 3, no. 1 (2002): 80. http://dx.doi.org/10.1038/nrg700-c1.

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Farkas, Donka F. "Bias and anti-bias." Approaches to Hungarian 18 2, no. 1 (2023): 96–126. http://dx.doi.org/10.1075/jul.00016.far.

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Abstract This paper proposes an account of the interpretive effects of two discourse particles in Hungarian, talán and vajon, within the view of context and context change developed in Farkas & Roelofsen (2017), and shows that the restrictions on their distribution follow from their interpretive properties. Building on Gyuris (2022), talán will be treated as signaling epistemic bias in both declaratives and interrogatives. Following Farkas (2022), vajon will be treated as a non-intrusive question marker, which, in the account proposed, is incompatible with bias markers. The restrictions on
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Haryati, Setyorini, T. Sihotang Ellen, and F. Sholikah. "Emotional Bias, Cognitive Bias and Herding Bias toward Investment Decision for Indonesian Investor." INTERNATIONAL JOURNAL OF SOCIAL SCIENCE HUMANITY & MANAGEMENT RESEARCH 2, no. 08 (2023): 785–92. https://doi.org/10.5281/zenodo.8228402.

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In making investment decisions, the role of emotions is very important because of investors irrational symptoms in decision making. This study aims to analyze the effect of emotional bias, cognitive bias, and herding bias on the investment decision making of investors in Indonesia. The research approach used is a quantitative approach. By using primary data from respondents spread throughout Indonesia, this study uses the partial least squares as a data analysis technique. The research hypothesis is that there is a positive influence of emotional bias, cognitive bias and herding bias in the pr
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G.S., Vidya, and Vijaygeetha M. "Bias in Epidemiology." Indian Journal of Preventive Medicine 4, no. 2 (2016): 75–81. http://dx.doi.org/10.21088/ijpm.2321.5917.4216.4.

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Baumgartner, Renate, and Sarah Kuhn. "Bias does not equal bias." TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis 30, no. 2 (2021): 69–70. http://dx.doi.org/10.14512/tatup.30.2.69.

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Thèses sur le sujet "Bias"

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Jayetileke, Harshanie Lakshika. "Bias, bias reduction and implications in predictive regression." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/210863/1/Harshanie%20Lakshika_Jayetileke_Thesis.pdf.

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Predictive regression models are often used to forecast future possibilities of a given finance variable. For that, we rely on statistical inference: estimation and hypothesis testing. Inaccurate estimation results make inaccurate inference for a scientific question. So, it is important to develop methodologies to reduce the bias in the estimation providing a sounder basis for statistical inference. Hence, the contribution of this research is to deliver more reliable estimators in terms of bias and the level of persistence of the predictor variable, and to develop a corresponding inferential f
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Gretton, Jeremy David. "Perceived Breadth of Bias as a Determinant of Bias Correction." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1499097376679535.

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Lockard, Andrea. "Examining Organizational Bias." Thesis, Lewis and Clark College, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10744391.

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<p> This study examined a feature of organizations that, prior to this work had not been identified or defined: Organizational Bias. If an organization can learn, have an identity and memory, then an organization can hold bias. Organizational bias occurs when an organization&rsquo;s culture, as defined and reproduced within its interactions with agents and actors, prevents actors from becoming agents by denying them the power required to change the organizational structure. This exclusionary aspect of bias creates a significant obstacle for educational institutions, many of whom define their m
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Brom-Pierzina, Jane. "Bias in children." Online version, 1999. http://www.uwstout.edu/lib/thesis/1999/1999brompierzinaj.pdf.

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Todd, Jemma Lauren. "Exploring the Role of Attention and Interpretation Biases in Understanding and Treating Pain." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17033.

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The processes that lead to the development and maintenance of chronic pain are still not well understood, however prominent theories and growing empirical research indicate that cognitive processes are likely to be relevant to pain. The aim of this thesis was therefore to investigate the role of attentional bias and interpretation bias in the experience of pain. Chapter 2 presents a meta-analysis of dot-probe studies investigating whether attentional biases exist, and found attentional biases towards sensory pain words for chronic pain patients compared to healthy individuals. Chapter 3 presen
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Hoeffel, Elizabeth Marie. "Gender Bias in Engineering: Does More Contact with Female Engineers Reduce Bias?" Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/31846.

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Status Characteristics Theory and Contact Theory are tested to measure gender bias in engineering students, and to determine if contact with female engineers helps reduce gender bias. To assess this, two versions of a resume, one with a femaleâ s name and one with a maleâ s name, were given to senior mechanical engineering students (n=225) to establish if they would rate the male applicant better than the female applicant. Respondents were asked how qualified they thought the respondent was, how much they would want the respondent on their team, and whether or not they would hire the appli
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梁恆新 and Hang-san Steven Leung. "Gender bias in policing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B42576702.

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Cook, Robert S. "Counselor bias against stepfamilies." Virtual Press, 1996. http://liblink.bsu.edu/uhtbin/catkey/1027107.

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Stepfamilies are the fastest growing family type in America. Unfortunately, our society holds unwarranted negative attitudes about and negative stereotypes of stepfamilies and stepfamily members. Research indicates that stepfamilies are not an inherently dysfunctional or deficient type of family. On the contrary, stepfamilies can be as healthy as nuclear families, and they can produce emotionally healthy individuals. Healthy stepfamilies, however, are often different in the roles of family members and the quality of interactions between members. It is this difference between stepfamily functio
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Hilbert, Anja, Sabrina Baldofski, Markus Zenger, Bernd Löwe, and Elmar Brähler. "Weight Bias Internalization Scale." Universitätsbibliothek Leipzig, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-148164.

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Objective: Internalizing the pervasive weight bias commonly directed towards individuals with overweight and obesity, cooccurs with increased psychopathology and impaired quality of life. This study sought to establish population norms and psychometric properties of the most widely used self-report questionnaire, the Weight Bias Internalization Scale (WBIS), in a representative community sample. Design and Methods: In a survey of the German population, N = 1158 individuals with overweight and obesity were assessed with the WBIS and self-report measures for convergent validation. Results: Item
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Leafhead, Katherine M. "Delusions and attentional bias." Thesis, Durham University, 1997. http://etheses.dur.ac.uk/5007/.

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A research method for investigating delusional beliefs is outlined by adopting the delusional belief that one is dead (the Cotard delusion) as a model delusion. Detailed analyses of published case reports of the Cotard delusion demonstrate that the term 'syndrome' as it is currently applied to the belief that one is dead is not helpful in terms of our understanding of the delusion. Four new case studies of the Cotard delusion suggest that preoccupation with belief may play a role in the formation and maintenance of delusions. Preoccupation with delusional belief was investigated using a varian
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Livres sur le sujet "Bias"

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Kligman, Robert D. Bias. Butterworths, 1998.

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Yt, Darmanto. Ditunggui naga: Bias-bias kejiwaan. Pustaka Pembangunan Swadaya Nusantara, 1994.

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Ulrich, Hoffrage, and Pohl Rüdiger, eds. Hindsight bias. Psychology, 2003.

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Christian, Sue Ellen. Overcoming Bias. 2nd ed. Routledge, 2021. http://dx.doi.org/10.4324/9780429356179.

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Gower, Kimberly, and Barbara Ritter. Understanding Bias. SAGE Publications, Inc., 2021. http://dx.doi.org/10.4135/9781071860250.

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Filimowicz, Michael. Systemic Bias. Routledge, 2022. http://dx.doi.org/10.4324/9781003173373.

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Chatfield, Tom, and Tom Chatfield. Overcoming Bias. SAGE Publications, Inc., 2022. http://dx.doi.org/10.4135/9781071880999.

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Schupp, Jürgen, and Christof Wolf, eds. Nonresponse Bias. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-10459-7.

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Mullainathan, Sendhil. Media bias. Massachusetts Institute of Technology, Dept. of Economics, 2002.

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Ruschmann, Paul. Media bias. Chelsea House Publishers, 2006.

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Chapitres de livres sur le sujet "Bias"

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Mercier, Hugo. "Confirmation bias – myside bias." In Cognitive Illusions, 3rd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003154730-7.

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Berry, Colin, Jason M. Meyer, Marjorie A. Hoy, et al. "Bias." In Encyclopedia of Entomology. Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_292.

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Fangerau, Heiner. "Bias." In Handbuch Ethik und Recht der Forschung am Menschen. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-35099-3_91.

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Heilbronner, Robert L. "Bias." In Encyclopedia of Clinical Neuropsychology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_949.

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Walton, Douglas. "Bias." In Applied Logic Series. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8632-0_7.

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Wideman, Timothy H., Michael J. L. Sullivan, Shuji Inada, et al. "Bias." In Encyclopedia of Behavioral Medicine. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_989.

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Nahler, Gerhard. "bias." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_114.

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Heilbronner, Robert L. "Bias." In Encyclopedia of Clinical Neuropsychology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_949-2.

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Munro, Paul, Hannu Toivonen, Geoffrey I. Webb, et al. "Bias." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_72.

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Vitek, Olga. "Bias." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1506.

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Actes de conférences sur le sujet "Bias"

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Riener, Christian, Herbert Hackl, David Pommerenke, Ralph Prestros, and Bernhard Auinger. "Evaluation of High-Voltage Bias-Tee for Capacitor Characterization under DC Bias." In 2024 International Symposium on Electromagnetic Compatibility – EMC Europe. IEEE, 2024. http://dx.doi.org/10.1109/emceurope59828.2024.10722746.

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Chen, Yuen, Vethavikashini Chithrra Raghuram, Justus Mattern, Rada Mihalcea, and Zhijing Jin. "Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias." In Findings of the Association for Computational Linguistics: NAACL 2025. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.findings-naacl.281.

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Chen, Danqing, Adithi Satish, Rasul Khanbayov, Carolin Schuster, and Georg Groh. "Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics." In Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.latechclfl-1.12.

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Yang, Yi, Hanyu Duan, Ahmed Abbasi, John P. Lalor, and Kar Yan Tam. "Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads." In Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.trustnlp-main.18.

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De-Arteaga, Maria, Alexey Romanov, Hanna Wallach, et al. "Bias in Bios." In FAT* '19: Conference on Fairness, Accountability, and Transparency. ACM, 2019. http://dx.doi.org/10.1145/3287560.3287572.

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D'Angelo, P., J. A. Pulido, T. Guardabrazo, P. Vieira, P. Silva, and F. Amarillo. "GNSS Bias Calibration System: GNSS-BICS system prototype." In 2012 6th ESA Workshop on Satellite Navigation Technologies (Navitec 2012) & European Workshop on GNSS Signals and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/navitec.2012.6423112.

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"Machine-learning discrimination: bias in, bias out." In 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2019. http://dx.doi.org/10.1109/icicis46948.2019.9014827.

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Nakkas, Alper, Kay Yut Chen, and Jie Zhang. "Aggregation Bias." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2019. http://dx.doi.org/10.24251/hicss.2019.814.

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Sohankar, Javad, Koosha Sadeghi, Ayan Banerjee, and Sandeep K. S. Gupta. "E-BIAS." In MSWiM'15: 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. ACM, 2015. http://dx.doi.org/10.1145/2815317.2815341.

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Wilkie, Colin, and Leif Azzopardi. "Algorithmic Bias." In CIKM '17: ACM Conference on Information and Knowledge Management. ACM, 2017. http://dx.doi.org/10.1145/3132847.3133135.

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Rapports d'organisations sur le sujet "Bias"

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Mullainathan, Sendhil, and Andrei Shleifer. Media Bias. National Bureau of Economic Research, 2002. http://dx.doi.org/10.3386/w9295.

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Velsko, S., and W. Thompson. Modeling Context Bias. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1097726.

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Hausman, Jerry. Sources of Bias and Solutions to Bias in the CPI. National Bureau of Economic Research, 2002. http://dx.doi.org/10.3386/w9298.

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Adam K. Baker, Adam K. Baker. Can we 'de-bias' someone? A Neuroscientific approach to decreasing bias. Experiment, 2016. http://dx.doi.org/10.18258/7373.

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van Wincoop, Eric, and Francis Warnock. Is Home Bias in Assets Related to Home Bias in Goods? National Bureau of Economic Research, 2006. http://dx.doi.org/10.3386/w12728.

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Huttenlocher, Janellen, and Larry V. Hedges. Bias in Reporting Location. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada277403.

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Blanchard, R. J. Sampler bias -- Phase 1. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/41305.

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Pope, Devin, Joseph Price, and Justin Wolfers. Awareness Reduces Racial Bias. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w19765.

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Gentzkow, Matthew, and Jesse Shapiro. Media Bias and Reputation. National Bureau of Economic Research, 2005. http://dx.doi.org/10.3386/w11664.

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Acemoglu, Daron. Equilibrium Bias of Technology. National Bureau of Economic Research, 2005. http://dx.doi.org/10.3386/w11845.

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