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@hex@kolektiva.social
2026-02-28 10:20:01

As salty as I am about it, there's also another way to think about this. For anyone who still has connections to folks on the right (which is perhaps unlikely for anyone on this server, I digress), the cult that has consumed them thrives on isolation and grievance.
The words "you were right" have the potential to cut through the programming and open up an opportunity for reconnection. The modern conspiratorial cult of the Right has been built partially around people who were told they were wrong or were crazy. In the vast majority of cases, they were wrong and even when they were right they completely misunderstood why, but we'll skip that for now. Liberals making fun of them (even the times when they definitely earned it) has pushed them further and further into their ideological hole.
The thing about those words, "you were right," in this context is that the way they offer reconnection also requires them to take one little step of betraying their ideology to accept them. So they must choose between maintaining allegiance to a pedophile or finally getting to feel superior after years of living in an illusion of persecution.
Under the ideology of the Right, admitting one is wrong is a weakness. It is admitting defeat. They have to "own the libs" by saying things, things that they know aren't true, in order to feel dominant. But these things are often so absurd that they end up being made fun of, feeling even more weak and pathetic, reinforcing their fear and alienation.
Offering what they're looking for can offer a way out, but only if they're willing to start to recognize the thing they've supported for what it is.
And they were right about some things. They were right that Bill Gates was a terrible person. I've had plenty of liberals defend him based on his philanthropy washing, but he's awful and always has been. The Epstein links make that blatant. They intuitively recognized him and didn't trust him, even if they were wildly off base about *how and why* he shouldn't be trusted... Even if their correct mistrust was leveraged into one of the most destructive conspiracy theories ever (vaccine denial and COVID vaccine avoidance).
They were right about Bill Clinton. He was always shady as fuck. Sure, the people who attacked him at the time turned out to be even more shady but that's not the point right now. He was connected to Epstein and that was always creepy as fuck.
And the Epstein thing was an open secret that liberals ignored for a long time. It was seen as some weird thing that right wing nutjobs believed about the Clintons. But it was true. Not all of it, and there has always been an antisemitic element to the right wing interpretation or Epstein stuff, but his whole pedophile conspiracy was always kind of real.
The whole "Illuminati"/deep state thing is a vast oversimplification, an attempt to make comprehensible an incredibly complex set of interlocking and emergent behaviors. But Epstein did very much want to remake the world, to create a new world order, and he absolutely played a part in it.
The Right wing nutjobs talked about global authoritarianism, Blackhawks flying over American cities, masked men with guns disarming and executing legal gun owners in the streets. That's all happening right now.
The "FEMA concentration camps" are not actually that far off. ICE and FEMA are sister agencies, both under DHS. I'd be more than happy to call that one "close enough" in order to hear some MAGA admit that ICE is, in fact, building concentration camps.
There was always a huge millennialist element to these things. They tended to be connected to "the antichrist." It was absurd, especially for me as someone who no longer identifies as a Christian. But I'll even acquiess that to a degree. The "the number of the Beast" is 666. That's just the sum of the Hebrew spelling of "Nero." Revelations focuses a lot on Nero coming back to life after his death. His death that involved a head wound, thus the line from Revelation 13:3:
> And I saw one of his heads as if it had been mortally wounded, and his deadly wound was healed. And all the world marveled and followed the beast.
The parallels between Trump and Nero are easy to draw, and Trump's ear wound feels pretty on-the-nose for this. I don't believe in "prophecy" in this way. I think that there are patterns, and useful patterns can become encoded in beleif systems. But I will, again, happily call this one "close enough" for anyone on that side willing to also acknowledge it. I'm happy to meet on that common ground, because anyone who accepts it must recognize that their duty is to fight against it.
A lot of these correct nuggets are embedded in a framework of religious extremism and antisemitism. The vast majority of the beliefs holding these together are wildly wrong and incredibly toxic. But by giving some room to feel validated, listened to, understood, can give some room to admit things that were wrong.
Cult de-programming starts with an opening. People have to talk through their own thoughts, hear their own inconsistencies. Guiding questions can help them untangle these things for themselves. And it all starts by having enough room to feel safe, to not feel cornered, to not feel stupid. Admitting mistakes means being vulnerable, and the MAGA cult is built on fear. It's built on exploiting vulnerability and locking it away.
De-programming takes a long time. It's not easy. It takes patience. But every person who comes out does so with a powerful perspective, a deep understanding, that can be turned back against it. The best people at getting people out of cults are former members. Some of the most dedicated antifa are former fascists who understood their mistakes and dedicate their lives to fixing them.

@arXiv_csCL_bot@mastoxiv.page
2026-03-31 11:12:28

Replaced article(s) found for cs.CL. arxiv.org/list/cs.CL/new
[1/5]:
- Beyond In-Distribution Success: Scaling Curves of CoT Granularity for Language Model Generalization
Ru Wang, Wei Huang, Selena Song, Haoyu Zhang, Qian Niu, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo
arxiv.org/abs/2502.18273 mastoxiv.page/@arXiv_csCL_bot/
- Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech
Anja Ryser, Yingqiang Gao, Sarah Ebling
arxiv.org/abs/2504.00780 mastoxiv.page/@arXiv_csCL_bot/
- Cultural Biases of Large Language Models and Humans in Historical Interpretation
Fabio Celli, Georgios Spathulas
arxiv.org/abs/2504.02572 mastoxiv.page/@arXiv_csCL_bot/
- BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
Jiageng Wu, et al.
arxiv.org/abs/2504.19467 mastoxiv.page/@arXiv_csCL_bot/
- Understanding the Anchoring Effect of LLM with Synthetic Data: Existence, Mechanism, and Potentia...
Yiming Huang, Biquan Bie, Zuqiu Na, Weilin Ruan, Songxin Lei, Yutao Yue, Xinlei He
arxiv.org/abs/2505.15392 mastoxiv.page/@arXiv_csCL_bot/
- Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat Falsehoods
Raza, Qureshi, Farooq, Lotif, Chadha, Pandya, Emmanouilidis
arxiv.org/abs/2505.17870 mastoxiv.page/@arXiv_csCL_bot/
- LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops
Fu, Jiang, Hong, Li, Guo, Yang, Chen, Zhang
arxiv.org/abs/2506.14493 mastoxiv.page/@arXiv_csCL_bot/
- GHTM: A Graph-based Hybrid Topic Modeling Approach with a Benchmark Dataset for the Low-Resource ...
Farhana Haque, Md. Abdur Rahman, Sumon Ahmed
arxiv.org/abs/2508.00605 mastoxiv.page/@arXiv_csCL_bot/
- Link Prediction for Event Logs in the Process Industry
Anastasia Zhukova, Thomas Walton, Christian E. Lobm\"uller, Bela Gipp
arxiv.org/abs/2508.09096 mastoxiv.page/@arXiv_csCL_bot/
- AirQA: A Comprehensive QA Dataset for AI Research with Instance-Level Evaluation
Huang, Cao, Zhang, Kang, Wang, Wang, Luo, Zheng, Qian, Chen, Yu
arxiv.org/abs/2509.16952 mastoxiv.page/@arXiv_csCL_bot/
- Multi-View Attention Multiple-Instance Learning Enhanced by LLM Reasoning for Cognitive Distortio...
Jun Seo Kim, Hyemi Kim, Woo Joo Oh, Hongjin Cho, Hochul Lee, Hye Hyeon Kim
arxiv.org/abs/2509.17292 mastoxiv.page/@arXiv_csCL_bot/
- Dual-Space Smoothness for Robust and Balanced LLM Unlearning
Han Yan, Zheyuan Liu, Meng Jiang
arxiv.org/abs/2509.23362 mastoxiv.page/@arXiv_csCL_bot/
- The Rise of AfricaNLP: Contributions, Contributors, Community Impact, and Bibliometric Analysis
Tadesse Destaw Belay, et al.
arxiv.org/abs/2509.25477 mastoxiv.page/@arXiv_csCL_bot/
- Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Reco...
Srivastav, Zheng, Bezzam, Le Bihan, Koluguri, \.Zelasko, Majumdar, Moumen, Gandhi
arxiv.org/abs/2510.06961 mastoxiv.page/@arXiv_csCL_bot/
- Neuron-Level Analysis of Cultural Understanding in Large Language Models
Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala, Hitomi Yanaka
arxiv.org/abs/2510.08284 mastoxiv.page/@arXiv_csCL_bot/
- CLMN: Concept based Language Models via Neural Symbolic Reasoning
Yibo Yang
arxiv.org/abs/2510.10063 mastoxiv.page/@arXiv_csCL_bot/
- Schema for In-Context Learning
Chen, Chen, Wang, Leong, Fung, Bernales, Aspuru-Guzik
arxiv.org/abs/2510.13905 mastoxiv.page/@arXiv_csCL_bot/
- Evaluating Latent Knowledge of Public Tabular Datasets in Large Language Models
Matteo Silvestri, Fabiano Veglianti, Flavio Giorgi, Fabrizio Silvestri, Gabriele Tolomei
arxiv.org/abs/2510.20351 mastoxiv.page/@arXiv_csCL_bot/
- LuxIT: A Luxembourgish Instruction Tuning Dataset from Monolingual Seed Data
Julian Valline, Cedric Lothritz, Siwen Guo, Jordi Cabot
arxiv.org/abs/2510.24434 mastoxiv.page/@arXiv_csCL_bot/
- Surfacing Subtle Stereotypes: A Multilingual, Debate-Oriented Evaluation of Modern LLMs
Muhammed Saeed, Muhammad Abdul-mageed, Shady Shehata
arxiv.org/abs/2511.01187 mastoxiv.page/@arXiv_csCL_bot/
toXiv_bot_toot