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Sarah Lang

@sarahalang.bsky.social

Head of Digital Humanities at Max Planck Institute for the History of Science (Berlin). Previously DH Graz. #Alchemy and early modern history of science & knowledge. #CriticalAI #CriticalDH #DH #ComputationalHumanities https://linktree.sarahalang.com/

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Sarah Lang@sarahalang.bsky.social · last wk.

More on the alchemical side… ⚗️ But here’s a sneak peak into our #ComputationalHumanities special issue 😍 Mediating Alchemical Language across Terminologies & Cultures in Ruland’s Lexicon Alchemiae:A Data-driven Study of Arabic Terms doi.org/10.1080/0002... #alchemy #dh #dh2026 #histsci #LLM

Mediating Alchemical Language across Terminologies and Cultures in Ruland’s Lexicon Alchemiae: A Data-driven Study of Arabic Terms

From the sixteenth century onwards, reference works emerged as means to transmit technical specialist knowledge to wider audiences. Authors, practitioners, and readers of early modern alchemy and c...

tandfonline.com

Sarah Lang@sarahalang.bsky.social · last mo.

Is computational history a thing now? 🤔 What does the term (and practice) even mean? 🤷‍♀️ I made an attempt… latex-ninja.com/2026/06/21/o... #dh #digitalhistory #digitalhumanities

Opportunities and Limitations of Computational History

By now, we have all heard of computational humanities. We knew of digital humanities and digital history, but now, all of a sudden, it seems that computational history is a thing. I will not get in…

latex-ninja.com

aelang.bsky.social@aelang.bsky.social · 2mo ago

For those interested in critical AI literacy, a hopeful thread: This past semester, for their final project, I asked my @uoe-llc.bsky.social Digital Humanities students to make "AI literacy objects" (AILOs): multimedia digital artefacts intended to foster critical literacy around generative AI +

Glitchy image with 'Cream of the Slop' in the center
Daniel Bellingradt@dbellingradt.bsky.social · 4mo ago

Working the morning shift as a scholar in 1540, a browser tab and a word document opened on two screens, other needed texts opened and handy, an overfull mail account nearby, and a hot beverage in reach to make for the best working condition. Bonus: wearing a thinking hat. #academicchatter

A man sitting at a working desk, in 1540 setting, with a drinking cup, a few books, a writing desk with paper sheets, and lots of more details.
Melanie Walsh@mellymeldubs.bsky.social · 2mo ago

This looks awesome! A data viz course from the authors of Data Feminism, @laurenfklein.bsky.social and @kanarinka.bsky.social. So excited to teach with these materials! openvisualizationacademy.org/courses/data...

A screenshot of one of the course pages from the Data Feminism for Data Visualization course. The text reads: "Course: Data Feminism for Data Visualization Authors: Lauren Klein Catherine D'Ignazio To get a certificate of completion, sign in to track your progress Content updated in October 2025 This course draws from the award-winning book Data Feminism (MIT Press, 2020), challenging students to consider how examining questions of gender, power, and justice can transform the way we visualize data. Drawing from the rich history of feminist activism and critical thought, we will learn to recognize and counter biases in conventional visualization practices while developing skills to create more ethical, inclusive, and socially responsible visualizations of our own. The course brings real-world examples of data visualization in conversation with theoretical frameworks that question whose perspectives are included (or not) in data science and data communication, illustrating the need for approaches that employ visualization to amplify minoritized perspectives and draw attention to issues of equity and justice. Chapters Introduction Introduction Chapter 1: Core Concepts & Foundational Principles Motivation and Definitions of Feminism Definitions of Power and Race Definitions of Gender Examine Power Challenge Power - Part A Challenge Power - Part B Quiz Test Your Knowledge Chapter 2: Data Feminism Principles & Examples Elevate Emotion and Embodiment - Part A Elevate Emotion and Embodiment - Part B Elevate Emotion and Embodiment - Part C Rethink Binaries and Hierarchies Embrace Pluralism - Part A Embrace Pluralism - Part B Consider Context Make Labor Visible Quiz Test Your Knowledge Chapter 3: Applying the Principles Challenging Deficit Perspectives Inclusive Language and Ordering Data Who is Missing? Who is Erased? Colors, Icons, Shapes, and People What’s Wrong With This Viz? Quiz Test Your Knowledge 3.4 Colors, Icons, Shapes, and People"
Alberto Cairo@albertocairo.com · 2mo ago

BIG NEWS in the Open Visualization Academy: openvisualizationacademy.beehiiv.com/p/data-femin... We're launching 'Data Feminism for Data Visualization' by @laurenfklein.bsky.social and @kanarinka.bsky.social #dataViz #infographics #dataVisualization #dataJournalism

Alan Liu@alanyliu.bsky.social · 2mo ago

Announcing the release of the online, open-access edition of _Critical Infrastructure Studies & Digital Humanities_ (in Debates in DH series): dhdebates.gc.cuny.edu/projects/cri.... Editors: Alan Liu, Urszula Pawlicka-Deger, James Smihties @jamessmithies.bsky.social). Print ed. appeared January 2026

Critical Infrastructure Studies and Digital Humanities | Debates in the Digital Humanities

This volume reimagines the digital humanities (DH) through the expanding field of critical infrastructure studies as it explores how DH builds on and extends the field’s theories and technologies. Inc...

dhdebates.gc.cuny.edu

Alan Liu@alanyliu.bsky.social · 8mo ago

U Minnesota Press's Spring 26 catalog, listing our new Critical Infrastructure Studies & Digital Humanities (in Debates in DH series), eds. Alan Liu, Urszula Pawlicka-Deger, @jamessmithies.bsky.social): z.umn.edu/spring26. Table of contents: www.upress.umn.edu/978151791608... @uminnpress.bsky.social

Screenshot of page 52 University of Minnesota Press Spring 2026 catalog, showing the book cover, description, and contributor names of the new book in the Debates in DH series (ed. Alan Liu, Urszula Pawlicka-Deger, and James Smithies) titled _Critical Infrastructrure Studies and Digital Humanities_. The text of the description reads as follows: How digital humanities can shape and be shaped by the infrastructures that sustain our world. _Critical Infrastructure Studies and Digital Humanities_ reimagines the digital humanities (DH) through the expanding field of critical infrastructure studies. Featuring voices from around the globe, this volume explores how DH builds on and extends theories and technologies of infrastructure that affect society, culture, and knowledge in different national and regional contexts. Examining DH’s own infrastructural genealogy, the contributors offer readers critical reflections and bold visions for the future as they address issues of environmentalism, decolonization, Indigenous sovereignty, multilingualism, labor justice, feminism, national development, and beyond from a variety of disciplinary perspectives embedded in concrete digital systems. Including innovative “infrastructure manifests,” the essays in this book illuminate how DH can both study and shape the systems that sustain culture, scholarship, and connection....
Sarah Lang@sarahalang.bsky.social · 2mo ago

When #DataFeminism @laurenfklein.bsky.social came out in 2020, it felt like a perfect match for #DigitalHumanities. Here was a framework all about concerns some #dh scholars have been circling for years. And yet, I can’t say the field has embraced it.😢 Why? latex-ninja.com/2026/05/31/w...

Why the Digital Humanities Haven’t Embraced Data Feminism

When Data Feminism came out in 2020, it felt like a perfect match for Digital Humanities. Here was a framework that took data seriously as a site of power, bias, and politics: exactly the kind of c…

latex-ninja.com

Olivia Guest · Ολίβια Γκεστ@olivia.science · 2mo ago

if any AI bro tries to claim to you (as they very often do generally) that we don't understand these models, remember: they are creating a false narrative even, especially, under scientific meanings of terms like "understanding"

Olivia Guest · Ολίβια Γκεστ@olivia.science · 3mo ago

Preprinted! Been working on this for ~1 year w wonderful coauthors Nancy & Mark: > Understanding Artificial Neural Networks: Mysterianism about Known Mechanism is Mysticism doi.org/10.5281/zeno... Have you noticed many experts say they don't understand AI? What's going on? 🤯 Let us tell you... 1/

Understanding Artificial Neural Networks: Mysterianism about Known Mechanism is Mysticism Olivia Guest1,2, Nancy Abigail Nuñez Hernández3, and Mark Blokpoel1,2 1Department of Cognitive Science and Artificial Intelligence, Radboud University, The Netherlands 2Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands 3Facultad de Estudios Superiores Acatlán-Universidad Nacional Autónoma de México, Mexico Mysterianism is the idea that human cognition, mind, cannot be understood. Taking this concept and applying it to known mechanism — such that claims are made that we do not know how engineered systems, such as artificial neural networks (ANNs), work, or that they constitute black boxes that we can only open with difficulty — is inappropriate at best and malicious at worst. We do know the mechanistic structure of such models because we designed and built them. We also do know their functional role (what they are for) as well as the mathematical function they are asked to approximate (map inputs to target outputs). Because mysterianist beliefs about known systems, such as ANNs, are often expressed, scientists need to sit up and take notice. We provide an error theory as to what is going on to help unpick this metatheoretical blunder. Ultimately, the problem is that ‘understanding’ is not a technical term in these cases: the word is co-opted for a specific narrative to sell ‘artificial intelligence’ through mystification. All computational systems, from pendulums to databases, will behave in ways we cannot predict or control — this is not a unique property of ANNs — and experts do indeed grasp the computational properties of these systems nonetheless. Keywords: artificial neural networks; mechanism; black box; epistemology; artificial intelligence; understanding
Philip N Cohen@philipncohen.com · 3mo ago

Two new facts stand out: 1. 85% of hallucinated citations in preprints are also in the subsequent journal version (thanks, peer review!) 2. Fake cites more likely to use the names of (male) scholars who are already highly cited, creating a fake-citation Matthew effect. arxiv.org/abs/2605.07723

LLM hallucinations in the wild: Large-scale evidence from non-existent citations

Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain p...

arxiv.org

Sarah Lang@sarahalang.bsky.social · 3mo ago

Looking for an overview on critical concerns of using LLMs? 🤔 Check out my chapter in: "Understanding Science with Large Language Models?" = www.transcript-verlag.de/978-3-8376-7... @arnosimons.bsky.social @gerd-grasshoff.bsky.social @transcript-verlag.bsky.social #dh #histsci #llm #ai

Understanding Science with Large Language Models?

How do LLMs change research about science, and create new insights and challenges for the humanities, social sciences, and AI?

transcript-verlag.de

Daniel van Strien@danielvanstrien.bsky.social · 3mo ago

The 'why' behind yesterday's demo: libraries don't (just) want searchable text from catalogue cards. They want structured records they can ingest into existing systems. Short blog + a live demo for this library use case: danielvanstrien.xyz/posts/2026/s...

How to turn catalogue card images into structured JSON with a 4B open model – Daniel van Strien

Re-OCR made digitised catalogue cards searchable as text. But libraries need structured records they can ingest into their catalogues. NuExtract3 (4B, Apache-2.0) extracts schema-shaped JSON from card...

danielvanstrien.xyz

Daniel van Strien@danielvanstrien.bsky.social · 3mo ago

NuExtract3 (4B, Apache-2.0) does OCR *and* structured extraction. Point it at a dataset of scanned index cards + a JSON schema → clean catalog JSON One command on huggingface Jobs. (or skip the schema for plain Markdown OCR) Script + dataset 👇

A worked example of NuExtract3 turning a document image into structured data. Top: a terminal command — hf jobs uv run --image vllm/vllm-openai nuextract3.py index-cards cards-json --template schema.json. Middle (input): a scanned typewritten library index card reading "ABAD (Joseph), Captain, Spanish Army, letter of (1783), 5538, f.11." Bottom (output): the JSON extracted from the card — image_type "index_card", heading "Abad J.", heading_type "person", epithet "Captain, Spanish Army", and one entry with ms_no "5538", folios ["f.11"], description "letter of (1783)".
Peter T. Evans@petertevans.bsky.social · 3mo ago

"Re-focusing on data also means rethinking what counts as meaningful work. Preparing data isn’t just a technical step; it carries so many interpretive choices. It’s where all our beloved hermeneutics live." Such a good rant...er...post by @sarahalang.bsky.social. Well worth your time to read.

Sarah Lang@sarahalang.bsky.social · 3mo ago

Are you also starting to get tired of the constant gesturing that we need better Research/ #AIEthics that doesn’t lead anywhere?😵‍💫 Better data --> better research. Sounds obvious, but that's not where the attention goes. A rant. And some answers ;) latex-ninja.com/2026/05/17/w... #DH

Sarah Lang@sarahalang.bsky.social · 3mo ago

Are you also starting to get tired of the constant gesturing that we need better Research/ #AIEthics that doesn’t lead anywhere?😵‍💫 Better data --> better research. Sounds obvious, but that's not where the attention goes. A rant. And some answers ;) latex-ninja.com/2026/05/17/w... #DH

Why Looking More Closely at Our Data Is the Way to Better Research Ethics

Better data leads to better research. That might sound obvious, but in practice, it’s often not where the attention goes. In conversations about AI, digital humanities, or computational research, t…

latex-ninja.com