https://en.wikipedia.org/wiki/Matthew_effect

The Matthew effect of accumulated advantage, sometimes called the Matthew principle, is the tendency of individuals to accrue social or economic success in proportion to their initial level of popularity, friends, and wealth. It is sometimes summarized by the adage or platitude "the rich get richer and the poor get poorer". The term was coined by sociologists Robert K. Merton and Harriet Zuckerman in 1968 and takes its name from the Parable of the Talents in the biblical Gospel of Matthew.

related somehow to the [[Lindy effect]]?

Der Bezos Earth Fund wird bis zum Ende des Jahrzehnts 10 Milliarden Dollar für den Kampf gegen die Klima und die Biodiversitätskrise zur Verfügung stellen. Die Mittel des Fonds geben ihm enormen Einfluss. Viele in der NGOs Szene sehen die Politik des Fonds als Gefährdung für die Unabhängigkeit der von ihm geförderten Organisationen. Der Guardian berichtet anlässlich einer Preisverleihung kritisch vor allem über das Engagement des Fonds für CO2 Kompensationen. https://www.theguardian.com/environment/article/2024/may/20/jeff-bezos-earth-fund-carbon-offsets-climate-sector-uneasy-aoe

Penggunaan Jejaring Semantik sebagai metode evaluasi Mata Kuliah.

https://new.nsf.gov/funding/opportunities/building-prototype-open-knowledge-network-proto

!- relationship between : Earth Commission and Science-Based Targets Network (SBTN) - Earth Commission results will be used to develop Science-Based targets - for businesses and cities

!- relationship with : Science-Based Targets Network

several key factors of attitude diffusion: 1. multiple, interacting attitudes 2. social influence between individuals 3. media influence

Explore this idea....

What does it look like quantitatively?

Horita, Y., & Yamazaki, M. (2022). Generalized and behavioral trust: Correlation with nominating close friends in a social network. PsyArXiv. https://doi.org/10.31234/osf.io/xu8k3

Vega-Oliveros, D. A., Grande, H. L. C., Iannelli, F., & Vazquez, F. (2021). Bi-layer voter model: Modeling intolerant/tolerant positions and bots in opinion dynamics. The European Physical Journal Special Topics, 230(14–15), 2875–2886. https://doi.org/10.1140/epjs/s11734-021-00151-8

Kumar, A., Chowdhary, S., Capraro, V., & Perc, M. (2021). Evolution of honesty in higher-order social networks. Physical Review E, 104(5), 054308. https://doi.org/10.1103/PhysRevE.104.054308

Kwiek, M. (2021). The Globalization of Science: The Increasing Power of Individual Scientists. MetaArXiv. https://doi.org/10.31222/osf.io/gj4aq

Mazumdar, S., & Thakker, D. (2020). Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks. Future Internet, 12(12), 210. https://doi.org/10.3390/fi12120210

https://www.mdpi.com/1999-5903/12/12/210

Marley, J., Blanche, M., Bulut, A., Bamber, L., McVay, S., Adeyanju, A., & Worsfold, S. (2021). The Digital Resilience Network [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/m8dbc

https://en.wikipedia.org/wiki/Evidence_board

We’ve analyzed thousands of COVID-19 misinformation narratives. Here are six regional takeaways—Bulletin of the Atomic Scientists. (n.d.). Retrieved August 1, 2021, from https://thebulletin.org/2021/06/weve-analyzed-thousands-of-covid-19-misinformation-narratives-here-are-six-regional-takeaways/

Leah Keating on Twitter: “This work with @DavidJPOS and @gleesonj is now on arXiv (https://t.co/hxjZnCmKcM): ‘A multi-type branching process method for modelling complex contagion on clustered networks’ Here is a quick overview of our paper: (1/6) https://t.co/3jQ2flhk71” / Twitter. (n.d.). Retrieved July 23, 2021, from https://twitter.com/leahakeating/status/1418150117106978816

Persoon, P. G. J. (2021). Cumulative structure and path length in networks of knowledge. ArXiv:2106.10480 [Physics]. http://arxiv.org/abs/2106.10480

Qian, Z.-Y., Yuan, C., Zhou, J., Chen, S.-M., & Nie, S. (2021). Optimal control of complex networks with conformity behavior. ArXiv:2106.10607 [Physics]. http://arxiv.org/abs/2106.10607

Agarwal, A. (2021). Ripple Effect of a Pandemic: Analysis of the Psychological Stress Landscape during COVID19. PsyArXiv. https://doi.org/10.31234/osf.io/dm5x2

Agarwal, A. (2021). The Accidental Checkmate: Understanding the Intent behind sharing Misinformation on Social Media. PsyArXiv. https://doi.org/10.31234/osf.io/kwu58

This is a great looking collection of articles on network science.

Karimi, Fariba, and Petter Holme. ‘A Temporal Network Version of Watts’s Cascade Model’. ArXiv:2103.13604 [Physics], 25 March 2021. http://arxiv.org/abs/2103.13604.

Devriendt, K., Martin-Gutierrez, S., & Lambiotte, R. (2020). Variance and covariance of distributions on graphs. ArXiv:2008.09155 [Physics, Stat]. http://arxiv.org/abs/2008.09155

Di Lauro, F., Berthouze, L., Dorey, M. D., Miller, J. C., & Kiss, I. Z. (2020). The impact of network properties and mixing on control measures and disease-induced herd immunity in epidemic models: A mean-field model perspective. ArXiv:2007.06975 [Physics, q-Bio]. http://arxiv.org/abs/2007.06975

Wang, Xiangrong, Alejandro Tejedor, Yi Wang, and Yamir Moreno. ‘Unique Superdiffusion Induced by Directionality in Multiplex Networks’. ArXiv:2011.00991 [Physics], 2 November 2020. http://arxiv.org/abs/2011.00991.

Hota, Ashish R., Tanya Sneh, and Kavish Gupta. ‘Impacts of Game-Theoretic Activation on Epidemic Spread over Dynamical Networks’. ArXiv:2011.00445 [Physics], 1 November 2020. http://arxiv.org/abs/2011.00445.

Heeren, A., HANSEEUW, B., Cougnon, L., & Lits, G. (2021, March 11). Excessive Worrying as the Driving Force of Anxiety During the First COVID-19 Lockdown-Phase in Belgium. https://doi.org/10.31234/osf.io/b34aj

Wang, X., Sirianni, A. D., Tang, S., Zheng, Z., & Fu, F. (2020). Public Discourse and Social Network Echo Chambers Driven by Socio-Cognitive Biases. Physical Review X, 10(4), 041042. https://doi.org/10.1103/PhysRevX.10.041042

Ogbunu, B. C. (2020, October 27). The Science That Spans #MeToo, Memes, and Covid-19. Wired. https://www.wired.com/story/the-science-that-spans-metoo-memes-and-covid-19/

And they probably shouldn't because typically different levels of people are making these decisions. Studio brass and producers typically have more to say about the lead roles and don't care as much about the smaller ones which are overseen by casting directors or sometimes the producers. The only person who has oversight of all of them is the director, and even then they may quit caring at some point.

This makes me wonder if she's cited Mark Granovetter or any of similar sociologists yet?

Apparently she did in footnote 32 in chapter 1. Ha!

There's some definite connection to this to network theory of those like Stuart Kaufmann. You don't need every node to be directly connected to create a robust network, particularly when there are other layers--here interpersonal connections, cellular, etc.

Lee, Hyeon-seung, Derek Dean, Tatiana Baxter, Taylor Griffith, and Sohee Park. ‘Deterioration of Mental Health despite Successful Control of the COVID-19 Pandemic in South Korea’. Preprint. PsyArXiv, 30 August 2020. https://doi.org/10.31234/osf.io/s7qj8.

r/BehSciAsk—Integrating Behavioural Science into Epidimiology. (n.d.). Reddit. Retrieved June 27, 2020, from https://www.reddit.com/r/BehSciAsk/comments/hg501h/integrating_behavioural_science_into_epidimiology/

Velásquez-Rojas, F., Ventura, P. C., Connaughton, C., Moreno, Y., Rodrigues, F. A., & Vazquez, F. (2020). Disease and information spreading at different speeds in multiplex networks. Physical Review E, 102(2), 022312. https://doi.org/10.1103/PhysRevE.102.022312

Khanam, K. Z., Srivastava, G., & Mago, V. (2020). The Homophily Principle in Social Network Analysis. ArXiv:2008.10383 [Physics]. http://arxiv.org/abs/2008.10383

Shahal, S., Wurzberg, A., Sibony, I., Duadi, H., Shniderman, E., Weymouth, D., Davidson, N., & Fridman, M. (2020). Synchronization of complex human networks. Nature Communications, 11(1), 3854. https://doi.org/10.1038/s41467-020-17540-7

Aleta, A., Arruda, G. F. de, & Moreno, Y. (2020). Data-driven contact structures: From homogeneous mixing to multilayer networks. PLOS Computational Biology, 16(7), e1008035. https://doi.org/10.1371/journal.pcbi.1008035

Supporting Open Science Data Curation, Preservation, and Access by Libraries. (2020, June 25). https://www.youtube.com/watch?v=SbmGWHpzAHs&feature=youtu.be

Fontana, M., Iori, M., Montobbio, F., & Sinatra, R. (2020). New and atypical combinations: An assessment of novelty and interdisciplinarity. Research Policy, 49(7), 104063. https://doi.org/10.1016/j.respol.2020.104063

Welcome! You are invited to join a webinar: Supporting Open Science Data Curation, Preservation, and Access by Libraries. After registering, you will receive a confirmation email about joining the webinar. (n.d.). Zoom Video. Retrieved June 28, 2020, from https://zoom.us/webinar/register/2615905946283/WN_W6dYUXQFTqGQjGAZPRB74w

Borsboom, D., Blanken, T., Dablander, F., Tanis, C., van Harreveld, F., & van Mieghem, P. (2020). BECON methodology [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/53ey9

UKRN position on covid 19 research. (2020 May 01). School of Psychological Science | University of Bristol. http://www.bristol.ac.uk/psychology/research/ukrn/news/2020/ukrn-position-on-covid-19-research.html

Jazayeri, A., & Yang, C. C. (2020). Motif Discovery Algorithms in Static and Temporal Networks: A Survey. ArXiv:2005.09721 [Physics]. http://arxiv.org/abs/2005.09721

Blanchard, M. A., & Heeren, A. (2020). Why We Should Move from Reductionism and Embrace a Network Approach to Parental Burnout? [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/y34cq

Katz, D. M., Coupette, C., Beckedorf, J., & Hartung, D. (2020). Complex Societies and the Growth of the Law. ArXiv:2005.07646 [Physics]. http://arxiv.org/abs/2005.07646

Zdeborová, L. (2020). Understanding deep learning is also a job for physicists. Nature Physics, 1–3. https://doi.org/10.1038/s41567-020-0929-2

Johnson, N.F., Velásquez, N., Restrepo, N.J. et al. The online competition between pro- and anti-vaccination views. Nature (2020). https://doi.org/10.1038/s41586-020-2281-1

Almaatouq, A., Noriega-Campero, A., Alotaibi, A., Krafft, P. M., Moussaid, M., & Pentland, A. (2020). Adaptive social networks promote the wisdom of crowds. Proceedings of the National Academy of Sciences, 201917687. https://doi.org/10.1073/pnas.1917687117

Hope, T., Borchardt, J., Portenoy, J., Vasan, K., & West, J. (2020, May 6). Exploring the COVID-19 network of scientific research with SciSight. Medium. https://medium.com/ai2-blog/exploring-the-covid-19-network-of-scientific-research-with-scisight-f75373320a8c

Pinto, S. F., & Ferreira, R. S. (2020). Analyzing course programmes using complex networks. ArXiv:2005.00906 [Physics]. http://arxiv.org/abs/2005.00906

About. (n.d.). Retrieved May 6, 2020, from https://coviz.apps.allenai.org/

Vasiliauskaite, V., & Rosas, F. E. (2020). Understanding complexity via network theory: A gentle introduction. ArXiv:2004.14845 [Physics]. http://arxiv.org/abs/2004.14845

Segovia-Martín, J., & Tamariz, M. (2020, May 5). Testing early and late connectivity dynamics in the lab: an experiment using 4-agent micro-societies. https://doi.org/10.31234/osf.io/nuf78

Chaves, M. S., Mattos, T. G., & Atman, A. P. F. (2020). Characterizing network topology using first-passage analysis. Physical Review E, 101(4), 042123. https://doi.org/10.1103/PhysRevE.101.042123

Collective Intelligence and COVID-19 | Trello. (n.d.). Retrieved April 20, 2020, from https://trello.com/b/STdgEhvX/collective-intelligence-and-covid-19