Tootfinder

The popular meaning of "luddite" is a straw-man. It's a sloppy word with a sloppy meaning now, and it's one we'd do well to watch out for.
The actual reality of who the Luddites were is far more interesting, the center of the hard-fought struggles against owners of factories disrupting entire towns and cities economies with massively terrible results, centralizing power and money and leaving a great number of people without any control of their work, formerly artisans who'd had a hand in their own work, and many automated out of jobs. Luddites destroyed automated looms not because they hated technology. They destroyed automated looms because they were taking the livelihood they depended on, with no recourse, and it was a disaster for a good while, and then millwork has gone from those places probably forever.
The problem now with LLMs and automated research systems is there's very little way for workers and creators to stick their shoes in the machinery. They've tried (https://arxiv.org/abs/2407.12281) but mostly failed, since unlike a factory full of textile workers, the equipment is remote, the automation virtual, an intangible software object that few can access in any meaningful way.

This is such a good summary of "the problem with 'data science'", as it tries to replace any domain expertise with the misguided notion that some form of statistics.
And so you end up with (maybe even well-meaning) researchers that are so far in over their head that they don't even know what they don't know.
Case in point: the authors of the preprint come from mathematics, computer science, physics, and 'future studies' (lol)…
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Arabic Hate Speech Identification and Masking in Social Media using Deep Learning Models and Pre-trained Models Fine-tuning
Salam Thabet Doghmash, Motaz Saad
https://arxiv.org/abs/2507.23661

Arabic Hate Speech Identification and Masking in Social Media using Deep Learning Models and Pre-trained Models Fine-tuning
Hate speech identification in social media has become an increasingly important issue in recent years. In this research, we address two problems: 1) to detect hate speech in Arabic text, 2) to clean a given text from hate speech. The meaning of cleaning here is replacing each bad word with stars based on the number of letters for each word. Regarding the first problem, we conduct several experiments using deep learning models and transformers to determine the best model in terms of the F1 score…

Language models align with brain regions that represent concepts across modalities
Maria Ryskina, Greta Tuckute, Alexander Fung, Ashley Malkin, Evelina Fedorenko
https://arxiv.org/abs/2508.11536

Language models align with brain regions that represent concepts across modalities
Cognitive science and neuroscience have long faced the challenge of disentangling representations of language from representations of conceptual meaning. As the same problem arises in today's language models (LMs), we investigate the relationship between LM--brain alignment and two neural metrics: (1) the level of brain activation during processing of sentences, targeting linguistic processing, and (2) a novel measure of meaning consistency across input modalities, which quantifies how consiste…