"One well-recognized illusion of generalizability arises when researchers mistake Western, Educated, Industrialized, Rich, Democratic (WEIRD) convenience samples as more representative of all humans than they actually are."
First, cognitive science is WEIRD: our participants aren’t sufficiently diverse to generalize across populations. An illusion of generalizability arises when we believe studies of WEIRD participants can generalize to all humans. 4/
November 11, 2025 at 1:47 PM
"One well-recognized illusion of generalizability arises when researchers mistake Western, Educated, Industrialized, Rich, Democratic (WEIRD) convenience samples as more representative of all humans than they actually are."
November 11, 2025 at 3:41 AM
Graph attention with structural features improves the generalizability of identifying functional sequences at a protein interface https://www.biorxiv.org/content/10.1101/2025.11.04.686550v1
November 11, 2025 at 2:45 AM
Graph attention with structural features improves the generalizability of identifying functional sequences at a protein interface https://www.biorxiv.org/content/10.1101/2025.11.04.686550v1
Graph attention with structural features improves the generalizability of identifying functional sequences at a protein interface https://www.biorxiv.org/content/10.1101/2025.11.04.686550v1
November 11, 2025 at 2:45 AM
Graph attention with structural features improves the generalizability of identifying functional sequences at a protein interface https://www.biorxiv.org/content/10.1101/2025.11.04.686550v1
Improving DNA Modeling with WaveDNA: Enhancing Speed, Generalizability, and Interpretability through Wavelet Transformation [new]
DNA to 2D via wavelets. Enables light, interpretable deep learning using CV.
DNA to 2D via wavelets. Enables light, interpretable deep learning using CV.
November 10, 2025 at 7:03 PM
Improving DNA Modeling with WaveDNA: Enhancing Speed, Generalizability, and Interpretability through Wavelet Transformation [new]
DNA to 2D via wavelets. Enables light, interpretable deep learning using CV.
DNA to 2D via wavelets. Enables light, interpretable deep learning using CV.
Improving DNA Modeling with WaveDNA: Enhancing Speed, Generalizability, and Interpretability through Wavelet Transformation https://www.biorxiv.org/content/10.1101/2025.11.07.687194v1
November 10, 2025 at 6:47 PM
Improving DNA Modeling with WaveDNA: Enhancing Speed, Generalizability, and Interpretability through Wavelet Transformation https://www.biorxiv.org/content/10.1101/2025.11.07.687194v1
Improving DNA Modeling with WaveDNA: Enhancing Speed, Generalizability, and Interpretability through Wavelet Transformation https://www.biorxiv.org/content/10.1101/2025.11.07.687194v1
November 10, 2025 at 6:47 PM
Improving DNA Modeling with WaveDNA: Enhancing Speed, Generalizability, and Interpretability through Wavelet Transformation https://www.biorxiv.org/content/10.1101/2025.11.07.687194v1
Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods
https://www.biorxiv.org/content/10.1101/2025.11.04.686644v1
November 10, 2025 at 1:36 PM
Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods
https://www.biorxiv.org/content/10.1101/2025.11.04.686644v1
Interpretable machine learning of clinical MALDI-TOF spectra discriminates carbapenem-resistant Klebsiella pneumoniae while revealing phylogenetic heterogeneity that limits model generalizability: Publication date: Available online 9 November 2025
Source: Journal of Mass… (JMSACL) #MassSpecRSS
Source: Journal of Mass… (JMSACL) #MassSpecRSS
Interpretable machine learning of clinical MALDI-TOF spectra discriminates carbapenem-resistant Klebsiella pneumoniae while revealing phylogenetic heterogeneity that limits model generalizability
Publication date: Available online 9 November 2025
Source: Journal of Mass Spectrometry and Advances in the Clinical Lab
Author(s): Chuangye Cai, Mengxue Zou, Mingxiao Chen, Peibo Yuan, Zhencheng Fang, Lanlan Zhong, Dingqiang Chen, Hongwei Zhou, Nianyi Zeng
dlvr.it
November 10, 2025 at 1:05 PM
Interpretable machine learning of clinical MALDI-TOF spectra discriminates carbapenem-resistant Klebsiella pneumoniae while revealing phylogenetic heterogeneity that limits model generalizability: Publication date: Available online 9 November 2025
Source: Journal of Mass… (JMSACL) #MassSpecRSS
Source: Journal of Mass… (JMSACL) #MassSpecRSS
Characterizing the Impact of Training Data on Generalizability: Application in Deep Learning to Estimate Lung Nodule Malignancy Risk https://doi.org/10.1148/ryai.240636 #lung #LungCancer #cancer
November 10, 2025 at 11:15 AM
Characterizing the Impact of Training Data on Generalizability: Application in Deep Learning to Estimate Lung Nodule Malignancy Risk https://doi.org/10.1148/ryai.240636 #lung #LungCancer #cancer
Interpretable machine learning of clinical MALDI-TOF spectra discriminates carbapenem-resistant Klebsiella pneumoniae while revealing phylogenetic heterogeneity that limits model generalizability #JMSACL www.sciencedirect.com/science/arti...
Interpretable machine learning of clinical MALDI-TOF spectra discriminates carbapenem-resistant Klebsiella pneumoniae while revealing phylogenetic heterogeneity that limits model generalizability
Carbapenem-resistant Klebsiella pneumoniae (CRKP) poses a significant public health threat. Rapid detection of CRKP and its resistance mechanisms is e…
www.sciencedirect.com
November 10, 2025 at 9:13 AM
Interpretable machine learning of clinical MALDI-TOF spectra discriminates carbapenem-resistant Klebsiella pneumoniae while revealing phylogenetic heterogeneity that limits model generalizability #JMSACL www.sciencedirect.com/science/arti...
Reviews that don’t appropriately evaluate based on the method have been killing me lately. If I had a dollar for every review of a qual paper that criticized a lack of generalizability…
Really unfortunate that a student led project got rejected for AERA b/c one of the reviewers didn’t understand what a design case is. Their feedback was that the lack of empirical data made it a poor proposal - when that’s not a design case feature or requirement.
November 9, 2025 at 7:19 PM
Reviews that don’t appropriately evaluate based on the method have been killing me lately. If I had a dollar for every review of a qual paper that criticized a lack of generalizability…
Limited generalizability of dynamic fMRI correlates of adolescent rumination
www.researchgate.net/publication/...
www.researchgate.net/publication/...
(PDF) Limited generalizability of dynamic fMRI correlates of adolescent rumination
PDF | On Oct 20, 2025, Isaac N. Treves and others published Limited generalizability of dynamic fMRI correlates of adolescent rumination | Find, read and cite all the research you need on ResearchGate
www.researchgate.net
November 8, 2025 at 12:01 PM
Limited generalizability of dynamic fMRI correlates of adolescent rumination
www.researchgate.net/publication/...
www.researchgate.net/publication/...
Limitations include the cross-sectional design and focus on a single cultural context, suggesting the need for further research to validate the generalizability of the results.
November 7, 2025 at 5:21 PM
Limitations include the cross-sectional design and focus on a single cultural context, suggesting the need for further research to validate the generalizability of the results.
November 7, 2025 at 11:07 AM
Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods https://www.biorxiv.org/content/10.1101/2025.11.04.686644v1
November 7, 2025 at 10:47 AM
Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods https://www.biorxiv.org/content/10.1101/2025.11.04.686644v1
Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods https://www.biorxiv.org/content/10.1101/2025.11.04.686644v1
November 7, 2025 at 10:47 AM
Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods https://www.biorxiv.org/content/10.1101/2025.11.04.686644v1
How can LLMs advance the cognitive sciences?
@ruimata.bsky.social and I put together a review on how LLMs can help us address five long-standing problems in cognitive science (e.g., disciplinary silos or lack of generalizability).
🔗 arxiv.org/2511.00206
@ruimata.bsky.social and I put together a review on how LLMs can help us address five long-standing problems in cognitive science (e.g., disciplinary silos or lack of generalizability).
🔗 arxiv.org/2511.00206
November 7, 2025 at 7:46 AM
How can LLMs advance the cognitive sciences?
@ruimata.bsky.social and I put together a review on how LLMs can help us address five long-standing problems in cognitive science (e.g., disciplinary silos or lack of generalizability).
🔗 arxiv.org/2511.00206
@ruimata.bsky.social and I put together a review on how LLMs can help us address five long-standing problems in cognitive science (e.g., disciplinary silos or lack of generalizability).
🔗 arxiv.org/2511.00206
Wenwen Li, Sizhe Wang, Hyunho Lee, Chenyan Lu, Sujit Roy, Rahul Ramachandran, Chia-Yu Hsu
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability
https://arxiv.org/abs/2511.04474
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability
https://arxiv.org/abs/2511.04474
November 7, 2025 at 7:38 AM
Wenwen Li, Sizhe Wang, Hyunho Lee, Chenyan Lu, Sujit Roy, Rahul Ramachandran, Chia-Yu Hsu
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability
https://arxiv.org/abs/2511.04474
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability
https://arxiv.org/abs/2511.04474
Li, Wang, Lee, Lu, Roy, Ramachandran, Hsu: Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability https://arxiv.org/abs/2511.04474 https://arxiv.org/pdf/2511.04474 https://arxiv.org/html/2511.04474
November 7, 2025 at 6:30 AM
Li, Wang, Lee, Lu, Roy, Ramachandran, Hsu: Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability https://arxiv.org/abs/2511.04474 https://arxiv.org/pdf/2511.04474 https://arxiv.org/html/2511.04474
the poor generalizability of any model output certainly is a function of the structure and redundancy of the training data but i think the greater point here is that human performance isn’t the aspirational goal
November 7, 2025 at 3:23 AM
the poor generalizability of any model output certainly is a function of the structure and redundancy of the training data but i think the greater point here is that human performance isn’t the aspirational goal
Technical sophistication: Research on sparse autoencoders, post-hoc attribution, multimodal reasoning failures, federation for generalizability. The work is moving toward explainability and auditability. Transparency tech advancing in parallel with capability.
November 6, 2025 at 4:48 AM
Technical sophistication: Research on sparse autoencoders, post-hoc attribution, multimodal reasoning failures, federation for generalizability. The work is moving toward explainability and auditability. Transparency tech advancing in parallel with capability.
At CHIL 2025, discussions emphasized user-centered AI explainability in healthcare, noting that success depends more on workflow integration than technical performance. Strategies such as federated learning aim to enhance machine learning models' generalizability. https://arxiv.org/abs/2510.15217
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
ArXiv link for Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
arxiv.org
November 6, 2025 at 2:31 AM
At CHIL 2025, discussions emphasized user-centered AI explainability in healthcare, noting that success depends more on workflow integration than technical performance. Strategies such as federated learning aim to enhance machine learning models' generalizability. https://arxiv.org/abs/2510.15217
Study: Most dermabrasion trials don't report the race of participants, limiting the generalizability of study results. buff.ly/YmDso8a via @medpagetoday.com #dermatology #clinicaltrials #clinicalresearch
Most Dermabrasion Trials Don't Report Race of Participants
Only about 20% of studies analyzed reported on race or skin color
buff.ly
November 6, 2025 at 1:24 AM
Study: Most dermabrasion trials don't report the race of participants, limiting the generalizability of study results. buff.ly/YmDso8a via @medpagetoday.com #dermatology #clinicaltrials #clinicalresearch
I get that comparative polisci colleagues are on alert abt the ecological fallacy/generalizability of the NY elections.
I also think there *is* an international politics dimension for all things US and its big and rich cities. Cant forget there's (helas) a hierarchy in the world, and the US matters
I also think there *is* an international politics dimension for all things US and its big and rich cities. Cant forget there's (helas) a hierarchy in the world, and the US matters
November 5, 2025 at 12:45 PM
I get that comparative polisci colleagues are on alert abt the ecological fallacy/generalizability of the NY elections.
I also think there *is* an international politics dimension for all things US and its big and rich cities. Cant forget there's (helas) a hierarchy in the world, and the US matters
I also think there *is* an international politics dimension for all things US and its big and rich cities. Cant forget there's (helas) a hierarchy in the world, and the US matters