harmful behavior may emerge through sequences of individually plausible steps
主流观点通常关注单个有害指令或直接的危险行为,但作者指出,计算机使用代理中的危险行为往往通过一系列看似合理的步骤累积产生。这一观点挑战了传统的安全评估方法,暗示我们需要关注代理的行为序列而非单一操作。
harmful behavior may emerge through sequences of individually plausible steps
主流观点通常关注单个有害指令或直接的危险行为,但作者指出,计算机使用代理中的危险行为往往通过一系列看似合理的步骤累积产生。这一观点挑战了传统的安全评估方法,暗示我们需要关注代理的行为序列而非单一操作。
Display product requirements, user flows, and design behaviors on each screen.
However, unevaluatedProperties has dynamic behavior, meaning that the set of properties to which it applies cannot be determined from static analysis of the schema (either the immediate schema object or any subschemas of that object).
annotation meta: may need new tag:
dynamic behavior vs. static analysis [not quite parallel]
or can we reuse something else like?: lexical semantics vs. run-time semantics
Rothmund, T., Farkhari, F., Azevedo, F., & Ziemer, C.-T. (2020). Scientific Trust, Risk Assessment, and Conspiracy Beliefs about COVID-19—Four Patterns of Consensus and Disagreement between Scientific Experts and the German Public. PsyArXiv. https://doi.org/10.31234/osf.io/4nzuy
American Medical Association (AMA). (2021, December 6). Peter Hotez, MD, PhD, on the omicron variant and Delta winter surge | COVID-19 Update for Dec. 6, 20. https://www.youtube.com/watch?v=WnfpC1_N2Mg
Kraemer, M. U. G., Hill, V., Ruis, C., Dellicour, S., Bajaj, S., McCrone, J. T., Baele, G., Parag, K. V., Battle, A. L., Gutierrez, B., Jackson, B., Colquhoun, R., O’Toole, Á., Klein, B., Vespignani, A., COVID-19 Genomics UK (COG-UK) Consortium‡, Volz, E., Faria, N. R., Aanensen, D. M., … Pybus, O. G. (2021). Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence. Science, 373(6557), 889–895. https://doi.org/10.1126/science.abj0113
Parsons, J. E., Newby, K. V., & French, D. P. (2018). Do interventions containing risk messages increase risk appraisal and the subsequent vaccination intentions and uptake? – A systematic review and meta‐analysis. British Journal of Health Psychology, 23(4), 1084–1106. https://doi.org/10.1111/bjhp.12340
Brewer, N. T., DeFrank, J. T., & Gilkey, M. B. (2016). Anticipated Regret and Health Behavior: A Meta-Analysis. Health Psychology : Official Journal of the Division of Health Psychology, American Psychological Association, 35(11), 1264–1275. https://doi.org/10.1037/hea0000294
Elgar, F. J., Stefaniak, A., & Wohl, M. J. A. (2020). The trouble with trust: Time-series analysis of social capital, income inequality, and COVID-19 deaths in 84 countries. Social Science & Medicine, 263, 113365. https://doi.org/10.1016/j.socscimed.2020.113365
Brodeur, Abel, Leonardo Baccini, and Stephen Weymouth. ‘The COVID-19 Pandemic and US Presidential Elections’. MetaArXiv, 10 November 2020. https://doi.org/10.31222/osf.io/sxajv.
Hyland, P., Vallières, F., Shevlin, M., Bentall, R. P., McKay, R., Hartman, T. K., McBride, O., & Murphy, J. (2021). Resistance to COVID-19 vaccination has increased in Ireland and the UK during the pandemic. PsyArXiv. https://doi.org/10.31234/osf.io/ry6n4
ReconfigBehSci on Twitter: ‘RT @PsyArXivBot: Resistance to COVID-19 vaccination has increased in Ireland and the UK during the pandemic https://t.co/AgKErDr7Yj’ / Twitter. (n.d.). Retrieved 2 March 2021, from https://twitter.com/SciBeh/status/1366707710151053312
McCarrick, D., Prestwich, A., Prudenzi, A., & O’Connor, D. (2021). Health Effects of Psychological Interventions for Worry and Rumination: A Meta-analysis. PsyArXiv. https://doi.org/10.31234/osf.io/bsf9e
Brewer NT, Chapman GB, Gibbons FX, Gerrard M, McCaul KD, Weinstein ND. Meta-analysis of the relationship between risk perception and health behavior: the example of vaccination. Health Psychol. 2007;26(2):136–45. doi:10.1037/0278-6133.26.2.136
Weingarten. E., Chen. Q., McAdams., Yi. J., (2016). From Primed Concepts to Action: A Meta-Analysis of the BehavioralEffects of Incidentally Presented Words. Psychological Bulletin 2016 (142) pp 472-497.
Fischer, R., & Karl, J. (2020). Predicting behavioral intentions to prevent or mitigate COVID-19: A meta-analysis. PsyArXiv. https://doi.org/10.31234/osf.io/ek69g
Behrens. F., Kret. M. (2020) Under the Umbrella of Prosocial Behavior – A Critical Comparison of Paradigms. PsyArXiv Preprints. Retrieved from: https://psyarxiv.com/9uebc/
Whitney R. Robinson on Twitter: “1/ An #EpiTwitter 🧵 about theory... https://t.co/rSjfkHG21r” / Twitter. (n.d.). Twitter. Retrieved August 18, 2020, from https://twitter.com/WhitneyEpi/status/1295522551892971520
Bhattacharya, C., Chowdhury, D., Ahmed, N., Ozgur, S., Bhattacharya, B., Mridha, S. K., & Bhattacharyya, M. (2020). The Nature, Cause and Consequence of COVID-19 Panic among Social Media Users in India. https://doi.org/10.31234/osf.io/dgr45
Litman. L,. Hartman. R., Jaffe. S., Robinson. J. (2020) County-level recruitment in online samples: Applications to COVID-19 and beyond. PsyArXiv Preprints. Retrieved from: https://psyarxiv.com/g3xw7/
COVID-19 Social Science Tracker - Google Sheets
Brooks, H. Z., Kanjanasaratool, U., Kureh, Y. H., & Porter, M. A. (2020). Disease Detectives: Using Mathematics to Forecast the Spread of Infectious Diseases [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/mvn9z
Crystal, J. (2020, May 18). Mobilizing behavioral scientists to respond to COVID-19. Psychonomic Society Featured Content. https://featuredcontent.psychonomic.org/mobilizing-behavioral-scientists-to-respond-to-covid-19/
Mancastroppa, M., Burioni, R., Colizza, V., & Vezzani, A. (2020). Active and inactive quarantine in epidemic spreading on adaptive activity-driven networks. ArXiv:2004.07902 [Cond-Mat, Physics:Physics]. http://arxiv.org/abs/2004.07902
Lanovaz, M., & Turgeon, S. (2020). Tutorial: Applying Machine Learning in Behavioral Research [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/9w6a3
Toxvaerd, F. M. O. (2020). Equilibrium Social Distancing [Working Paper]. Faculty of Economics, University of Cambridge. https://doi.org/10.17863/CAM.52489
Zoder-Martell, K., Markelz, A., Floress, M. T., Skriba, H. A., & Sayyah, L. E. N. (2020, May 6). Technology to Facilitate Telehealth in Applied Behavior Analysis. https://doi.org/10.31234/osf.io/nz5s7
Du, H., Yang, J., King, R. B., Yang, L., & Chi, P. (2020). COVID-19 Increases Online Emotional and Health-Related Searches [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/5gskw
Vilella, S., Paolotti, D., Ruffo, G. et al. News and the city: understanding online press consumption patterns through mobile data. EPJ Data Sci. 9, 10 (2020). https://doi.org/10.1140/epjds/s13688-020-00228-9
Horstmann, K. T., Rauthmann, J. F., Sherman, R. A., & Ziegler, M. (2020, April 30). Unveiling an Exclusive Link: Predicting Behavior with Personality, Situation Perception, and Affect in a Pre-Registered Experience Sampling Study. Retrieved from psyarxiv.com/ztw2n
Tarbox, C., Silverman, E. A., Chastain, A. N., Little, A., Bermudez, T. L., & Tarbox, J. (2020, April 30). Taking ACTion: 18 Simple Strategies for Supporting Children with Autism During the COVID-19 Pandemic. Retrieved from psyarxiv.com/96whj
Moyers, S. A., & Hagger, M. S. (2020, April 20). Physical activity and sense of coherence: A meta-analysis. https://doi.org/10.31234/osf.io/d9e3k
Dai, B., Fu, D., Meng, G., Qi, L., & Liu, X. (2020, April 25). The effects of governmental and individual predictors on COVID-19 protective behaviors in China: a path analysis model. https://doi.org/10.31234/osf.io/hgzj9
Wang, T., Chen, X., Zhang, Q., & Jin, X. (2020, April 26). Use of Internet data to track Chinese behavior and interest in COVID-19. https://doi.org/10.31234/osf.io/j6m8q
Colombo, R., Wallace, M., & Taylor, R. S. (2020, April 11). An Essential Service Decision Model for Applied Behavior Analytic Providers During Crisis. https://doi.org/10.31234/osf.io/te8ha
Giangreco, G. (n.d.). Case fatality rate analysis of Italian COVID-19 outbreak. Journal of Medical Virology, n/a(n/a). https://doi.org/10.1002/jmv.25894