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William J. Brady

@williambrady.bsky.social
5.9K followers 369 following 170 posts

Associate prof @ Kellogg School of Management, Northwestern University. Studying emotion, morality, social networks, psych of tech. #firstgen college graduate

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Reposted by William J. Brady
Kevin Elliott @kjephd.bsky.social · 23/09/2026
AI is the Gyges' ring of student work: it offers perfect concealment of wrongdoing. If Glaucon is right, then everyone will use it to do injustice (cheat), & will be wise to do so. If Socrates/Plato is right, then doing so also harms the wrong-doer, whose soul will be malformed into misery by it.
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William J. Brady @williambrady.bsky.social · 09/09/2026
Glad to see a revised version of this out now at Current Opinion in Psychology! www.sciencedirect.com/science/arti...
sciencedirect.com
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William J. Brady @williambrady.bsky.social · 04/09/2026
Registration is open for the 4th annual Computational Psychology preconference at @spspnews.bsky.social Annual conference in Philly 🎊 We are on for a full day on Thursday, Feb 11. 3 keynote themes + a debate that couldn't be more timely 🧵👇
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William J. Brady @williambrady.bsky.social · 02/09/2026
Come join me @kelloggschoolnu.bsky.social. The management department is hiring for position of Assistant Professor. We are a fun cross-disciplinary group, so if your training is in psychology, sociology, OB or computational social science, etc you should check out the position! Apps due in 2 weeks!
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William J. Brady @williambrady.bsky.social · 27/08/2026
Excited to work on this with a great team ✨ Stay tuned for some hiring announcements if you're interested in these topics!
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Mohammad Atari @mohammadatari.bsky.social · 20/07/2026
New paper out today in Social and Personality Psychology Compass! What happens when our moral values come into conflict? Our new paper explores "metamorality": the second-order principles we use to navigate conflicting moral choices. W/ Jesse Graham
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William J. Brady @williambrady.bsky.social · 29/06/2026
In 2023 we showed how engagement-based algorithms exploit human social learning biases, distorting how we learn what's normal, common, or credible from each other ("Algorithm-mediated social learning," Trends in Cognitive Sciences). The obvious follow-up: where does generative AI fit in?
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Jason Koebler @jasonkoebler.bsky.social · 15/06/2026
New: Researchers have quantified how easy AI search is to manipulate. Just 13 words buried in a random Reddit comment can poison AI search results. They suggest this is not easy to stop: "The way you can attack these systems is so much dumber than you think it is" www.404media.co/it-is-trivia...
404media.co
It Is Trivially Easy to Use Reddit to Manipulate AI Search, Research Suggests
"We show that a tiny snippet—just 13 words—of retrieved text on a UGC website like Reddit, Wikipedia, Quora, or Facebook can change AI agents to output spam / scam content pretty consistently."
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Matthew Facciani @matthewfacciani.bsky.social · 29/05/2026
Researchers looked at people’s positions on specific issues, rather than just whether they identified as conservative or liberal. They found 43% of self-identified conservatives supported mostly left-leaning policies on topics like spending, climate action, immigration, and vaccine mandates.
academic.oup.com
Thinking Ideologically: The Limited Role of Left and Right Labels as Policy Shortcuts
Abstract. How do voters use left-right ideological labels as shortcuts for policy positions in evaluating electoral candidates? We offer a distinction betw
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William J. Brady @williambrady.bsky.social · 27/05/2026
As I mentioned in the below thread, this project involved many feats of engineering, led by the fantastic @markptorres.bsky.social. If you're a CS or CSS person interested in the gory details, see his blog post: markptorres.com/research/202...
markptorres.com
How we built the infrastructure for a large-scale social media field experiment during the 2024 US election
What we built
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William J. Brady @williambrady.bsky.social · 27/05/2026
✨New paper out @nature.com ✨ For 8 weeks around the 2024 US election, we randomly assigned 2,000 people to use social media algos we built ourselves. Do engagement-based algorithms amplify intergroup, moral & emotional (IME) content—and does that distort how we see political norms? 🧵🔗 👇
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Joshua Tucker @jatucker.bsky.social · 14/05/2026
This paper benefitted enormously from the very involved and insightful editorial team @nature.com, led by @meharpist.bsky.social.
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Association for Psychological Science @psychscience.bsky.social · 07/05/2026
Meet @williambrady.bsky.social, an assistant professor of management and organizations at @kelloggschoolnu.bsky.social and a 2026 APS Spence Award recipient!
psychologicalscience.org
Member Spotlight: 2026 Spence Awardee William Brady on Blazing Your Own Research Path
The Spence Award recipient answered a few questions about his research on the interactions between human psychology and technology, the highlights of his career, and his practical advice for future re...
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Berna Devezer @devezer.bsky.social · 30/04/2026
📣 The difference between replicable and not replicable is not itself scientifically replicable. 📣 New work with Erkan Buzbas, showing that verdicts such as "X% of results replicated" are based on an inferential machinery that doesn't work. arxiv.org/abs/2604.26268
Screenshot of a paper's title page. Title: "The Difference Between 'Replicable' and 'Not replicable' is not Itself Scientifically Replicable". Authors: Berna Devezer and Erkan O. Buzbas, both at the University of Idaho — Devezer in the Department of Business and the Institute for Modeling Collaboration and Innovation, Buzbas in the Department of Mathematics and Statistical Science. Authors contributed equally. Corresponding author: bdevezer@uidaho.edu.
Abstract: Replication studies estimate the replicability rate of scientific results by aggregating binary verdicts of experiments. Exact replications are rarely attainable, so most replication sequences are non-exact. Experiments differ in ways that matter and do not share a single common data-generating process. We formalize two statistical interpretations of this non-exactness. In a shared latent rate model (benchmark), experiments are exchangeable and depend on a common random replicability rate. In a conditionally independent rates model (operational), each experiment has its own replicability rate drawn independently from a population distribution. Under the shared latent rate model, even small variability among replicability rates induces an irreducible variance floor on the estimated mean replicability rate that cannot be eliminated by adding more replications. Under the conditionally independent rates model, the degree of non-exactness is not identifiable from standard replication data, because one binary verdict per experiment contains no information about between-experiment heterogeneity. Researchers therefore cannot tell which precision regime they are operating in or whether high- and low-replicability sequences can be distinguished in principle. As a result, the usual data structure of one binary verdict per experiment cannot support reliable demarcation between "replicable" and "not replicable" results and systematically understates uncertainty, making high- and low-replicability sequences appear discrim…
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J. Nathan Matias @natematias.bsky.social · 27/04/2026
Are you a leader trying to figure out how to evaluate an AI system? Are you in business, journalism, civil society, or the tech industry? I'm excited to share the publication of the book I wish I had in my first job testing AI seventeen years ago: "Auditing AI." mitpress.mit.edu/978026205172...
J. Nathan Matias, with a book entitled "Auditing AI" in front of the Big Red Barn at Cornell University.A picture of the book "Auditing AI" in front of the ice cream machines at Cornell University's Dairy Bar.
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Robb Willer @robbwiller.bsky.social · 22/04/2026
Climate change impacts are here, but public support for action remains polarized. Which climate messages move people? In a ~13,500-person megastudy, we tested 10 of the most-cited messages. Six increased pro-environmental attitudes in the U.S.—but only by 1–4 percentage points. 🧵
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Petter Törnberg @pettertornberg.com · 22/04/2026
Is social media dying? How much did Twitter change as it became X? Which party now dominates the conversation? Using nationally representative ANES data from 2020 & 2024, I map how the U.S. social media landscape has changed Here are the key take-aways 🧵 Full paper out now in in JQD:DM!
journalqd.org
Shifts in U.S. Social Media Use, 2020–2024: Decline, Fragmentation, and Enduring Polarization | Journal of Quantitative Description: Digital Media
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David Amodio @davidamodio.bsky.social · 07/04/2026
New preprint! w/Tessa Charlesworth & @williambrady.bsky.social: The Psychology of Algorithmic Bias We introduce a psychology-centered framework to specify mechanisms through which human behavior interacts dynamically with AI systems to produce algorithmic bias. osf.io/preprints/psyarxiv/rxu37_v1
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Maya Rossignac-Milon @mrossignacmilon.bsky.social · 26/03/2026
New preprint! ✨ Do you and your partner have made-up words ("eggy" to mean awkward)? Do you and your bestie have an anecdote you love to tell together (that time one of you tripped over an acorn)? Do you and your closest colleague have a cherished ritual (weekly lunch at "the usual spot")? 🧵
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Judy Kim @joodykeem.bsky.social · 28/02/2026
They double-booked a room for the "Healthy, Wise, Wealthy, Decision Making" and "Moral Machines" sessions at SPSP, but @williambrady.bsky.social exercised some masterful (& healthy & wise & moral & wealthy?) negotiation skills and we're now getting two sessions of talks in one 😊
Photo of Billy talking to a crowd of confused speakers & attendees Photo of negotiations continuing between speakers
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William J. Brady @williambrady.bsky.social · 28/02/2026
@killianmcloughlin.bsky.social social media news especially likely to use villian / victim framing; low quality news especially; more likely to evoke outrage and draw engagement when doing so #spsp
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William J. Brady @williambrady.bsky.social · 26/02/2026
New work from @drsanaz.bsky.social : using smart phone sensing data, one of the biggest predictors of people higher on authoritarian measure was Facebook and social media use #spsp
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Jessica Hullman @jessicahullman.bsky.social · 18/12/2025
Many think LLM-simulated participants can transform behavioral science. But there's been a lack of accessible discussion of what it means to validate LLMs for behavioral scientists. Under what conditions can we trust LLMs to learn about human parameters? Our paper maps the validation landscape. 1/
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William J. Brady @williambrady.bsky.social · 24/02/2026
We're excited about the upcoming Computational Psychology preconference at @spspnews.bsky.social this Thursday. See our action-packed full day agenda below! Featuring 3 keynote talk themes with related early-career speakers, data blitz session, panel discussion. Don't miss it! #SPSP
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M.J. Crockett @mjcrockett.bsky.social · 24/02/2026
Last year at SPSP we had some great discussions about research with LLMs. This time #spsp2026 we're back with a whole workshop! Friday 2/27 at 8am, + informal drinks at 6pm to continue the conversation More info, plus a chance to submit discussion topics, here: spsp2026.carrd.co
spsp2026.carrd.co
Ethical and Reproducible use of Large Language Models for Data Analysis | SPSP 2026 Professional
Ethical and Reproducible use of Large Language Models for Data Analysis | SPSP 2026 Professional
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Curtis Puryear @curtispuryear.bsky.social · 23/02/2026
Interested in why moral conflict is so common on social media? Join us at #SPSP 2026 for our symposium. We’ll present new findings on how platforms shape digital discourse and explore pathways toward healthier online environments 🗓 Saturday, the 28th | 9:30–10:40 AM 📍 Room E270, Level 2
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William J. Brady @williambrady.bsky.social · 23/02/2026
Very honored by this one! Thanks to all my mentors, students and colleagues who made it possible! And congrats to all the recipients for their amazing work.
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Joshua Conrad Jackson @joshcjackson.bsky.social · 21/02/2026
For folks interested in learning about our lab's research, check out this flier with all our presentations at this coming #SPSP2026 conference @spspnews.bsky.social. With research by several rising stars covering tech, culture, politics and more Credit to our talented lab manager Hanying Yao!
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William J. Brady @williambrady.bsky.social · 26/01/2026
Congrats to @mjcrockett.bsky.social on the Troland Research Award from @nasonline.org ! Having witnessed the "unusual achievement" first hand, very happy to see the recognition 🌹💐 www.nasonline.org/award/trolan...
nasonline.org
Troland Research Award – NAS
Two Troland Research Awards of $75,000 are given annually to recognize unusual achievement by early-career researchers (preferably 45 years of age or younger) and to further empirical research within ...
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Yuanze Liu @yuanzeliu.bsky.social · 31/12/2025
Before the end of this year, I’m glad to share a short perspective/policy piece, recently out with @joshcjackson.bsky.social , Zhao Wang, and @williambrady.bsky.social: “Large AI Models Have a Prioritization Problem: Policy Implications and Solutions.”
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M.J. Crockett @mjcrockett.bsky.social · 11/12/2025
New preprint: Empathy, Thick and Thin papers.ssrn.com/sol3/papers.... It is perhaps foolhardy to attempt to say something new about a topic as widely studied as empathy. I tried anyway! 1/

Abstract

When we empathize with someone going through something, we often draw on our past experiences with the someone and the something. These kinds of experiences ground "thick empathy", a form of empathy that has been largely overlooked in the psychology and neuroscience literature. Consider how a mother, empathizing with her daughter about to give birth, can draw on her own experience of childbirth, and her relationship with her daughter, to deeply grasp what her daughter is going through in a way that others who lack those experiences cannot. I argue that thick empathy deserves more empirical attention because it is associated with well-being and helps us build networks of effective mutual social support. My analysis highlights novel risks and dilemmas posed by "empathy machines" that promise to enhance or even replace human empathy and are becoming increasingly popular as a potential solution to widespread loneliness. Even when empathy machines provide value to individuals, their widespread adoption risks imposing collective emotional and epistemic costs that ultimately make it harder for us to empathize well.

Keywords: empathy, understanding, experience, thick description, ethnography, phenomenal knowledge, interpersonal knowledge, virtual reality, artificial intelligence, chatbots
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Lisa Fazio @lkfazio.bsky.social · 11/12/2025
New from me - how AI-generated political videos have become just another part of social media, used to entertain, outrage and monitize attention theconversation.com/ai-generated...
theconversation.com
AI-generated political videos are more about memes and money than persuading and deceiving
Don’t discount the threat of AI political videos fooling people, but for now, they’re mostly about bolstering group identity and cashing in on viral content.
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Rebecca Frazer @beccafraz.bsky.social · 24/11/2025
Out now in Scientific Reports! Despite high correlations, ChatGPT models failed to replicate human moral judgments. We propose tests beyond correlation to compare LLM data and human data. With @mattgrizz.bsky.social @andyluttrell.bsky.social @chasmonge.bsky.social www.nature.com/articles/s41...
nature.com
ChatGPT does not replicate human moral judgments: the importance of examining metrics beyond correlation to assess agreement - Scientific Reports
Scientific Reports - ChatGPT does not replicate human moral judgments: the importance of examining metrics beyond correlation to assess agreement
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Sasha Gusev @sashagusevposts.bsky.social · 21/11/2025
So there you have it, twin study estimates were greatly inflated, and molecular data sets the record straight. I walk through possible counter-arguments, but ultimately the uncomfortable truth is that genes contribute to traits much less than we always thought.
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William J. Brady @williambrady.bsky.social · 18/11/2025
Great work by @natematias.bsky.social & Megan Price: public involvement in AI is an important part of rigorous science. AI systems are sociotechnical, meaning that the lived experience of the public is essential for validation, etc. www.pnas.org/doi/10.1073/...
pnas.org
How public involvement can improve the science of AI | PNAS
As AI systems from decision-making algorithms to generative AI are deployed more widely, computer scientists and social scientists alike are being ...
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Jonathan Doriscar @jonds7.bsky.social · 17/11/2025
New preprint out 📄 “Why Reform Stalls: Justifications of Force Are Linked to Lower Outrage and Reform Support.” Why do some cases of police violence spark reform while others fade? We look at how people explain them—through justification or outrage. osf.io/preprints/ps...
osf.io
OSF
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David Rand @dgrand.bsky.social · 14/11/2025
🚨Out in PNAS🚨 Examining news on 7 platforms: 1)Right-leaning platforms=lower quality news 2)Echo-platforms: Right-leaning news gets more engagement on right-leaning platforms, vice-versa for left-leaning 3)Low-quality news gets more engagement EVERYWHERE - even BlueSky! www.pnas.org/doi/10.1073/...
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Mark Ho @markkho.bsky.social · 13/11/2025
Excited to share a new preprint, accepted as a spotlight at #NeurIPS2025! Humans are imperfect decision-makers, and autonomous systems should understand how we deviate from idealized rationality Our paper aims to address this! 👀🧠✨ arxiv.org/abs/2510.25951 a 🧵⤵️
arxiv.org
Estimating cognitive biases with attention-aware inverse planning
People's goal-directed behaviors are influenced by their cognitive biases, and autonomous systems that interact with people should be aware of this. For example, people's attention to objects in their...
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William J. Brady @williambrady.bsky.social · 11/11/2025
✨New preprint! Why do people express outrage online? In 4 studies we develop a taxonomy of online outrage motives, test what motives people report, what they infer for in- vs. out-partisans, and how motive inferences shape downstream intergroup consequences. Led by @felix-chenwei.bsky.social 🧵👇
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Jay Van Bavel, PhD @jayvanbavel.bsky.social · 05/11/2025
These has been sharp rise in moralized language on social media Two processes explained this shift: (1) within-user increases in moral language over time (2) highly moralized users became more active while less moralized users disengaged osf.io/preprints/ps...
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Petter Törnberg @pettertornberg.com · 30/10/2025
Posting is correlated with affective polarization: 😡 The most partisan users — those who love their party and despise the other — are more likely to post about politics 🥊 The result? A loud angry minority dominates online politics, which itself can drive polarization (see doi.org/10.1073/pnas...)
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William J. Brady @williambrady.bsky.social · 28/10/2025
Reminder to apply to the DRRC postdoc fellowship! Deadline is this week.
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Paul Smaldino @psmaldino.bsky.social · 27/10/2025
Re-posting this because I really like it and I think we need to understand identity from a functionalist perspective more than ever. osf.io/preprints/ps...
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Carl T. Bergstrom @carlbergstrom.com · 24/10/2025
1. We ( @jbakcoleman.bsky.social, @cailinmeister.bsky.social, @jevinwest.bsky.social, and I) have a new preprint up on the arXiv. There we explore how social media companies and other online information technology firms are able to manipulate scientific research about the effects of their products.
Three schematic diagrams. The first illustrates selective publishing of internal resection, the second selective causal focus, and the third selective access and funding for researchers.
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Joe Bak-Coleman @jbakcoleman.bsky.social · 22/10/2025
Great piece on the absurdity of brute force multiverse analyses. www.pnas.org/doi/10.1073/...
pnas.org
Robustness is better assessed with a few thoughtful models than with billions of regressions | PNAS
Robustness is better assessed with a few thoughtful models than with billions of regressions
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M.J. Crockett @mjcrockett.bsky.social · 21/10/2025
Can AI simulations of human research participants advance cognitive science? In @cp-trendscognsci.bsky.social, @lmesseri.bsky.social & I analyze this vision. We show how “AI Surrogates” entrench practices that limit the generalizability of cognitive science while aspiring to do the opposite. 1/
sciencedirect.com
AI Surrogates and illusions of generalizability in cognitive science
Recent advances in artificial intelligence (AI) have generated enthusiasm for using AI simulations of human research participants to generate new know…
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William J. Brady @williambrady.bsky.social · 20/10/2025
Last call for data-blitz and poster submission for the Computational Psychology preconference @spspnews.bsky.social! See thread below for details and hope to see you in Chicago!
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Steve Rathje @steverathje.bsky.social · 01/10/2025
🚨 New preprint 🚨 Across 3 experiments (n = 3,285), we found that interacting with sycophantic (or overly agreeable) AI chatbots entrenched attitudes and led to inflated self-perceptions. Yet, people preferred sycophantic chatbots and viewed them as unbiased! osf.io/preprints/ps... Thread 🧵
Abstract and results summary
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Jay Van Bavel, PhD @jayvanbavel.bsky.social · 30/09/2025
Our new paper finds that AI can overcome partisan #bias We find that AI sources are preferred over ingroup and outgroup sources--even when people know both are equally accurate (N = 1,600+): osf.io/preprints/ps...
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William J. Brady @williambrady.bsky.social · 29/09/2025
The computational psych preconference is back @spspnews.bsky.social for a full day! This year's lineup: 👉theory-driven modeling: Hyowon Gweon 👉data-driven discovery: @clemensstachl.bsky.social 👉application: me 👉 panel: @steveread.bsky.social Sandra Matz, @markthornton.bsky.social Wil Cunningham
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