Plainbook makes data-science notebooks natural-language-first by preserving cell descriptions, generating code via AI, enforcing linear execution via a checkpointing kernel, and adding value-centered cell and global tests.
Apress, Berkeley, CA
4 Pith papers cite this work, alongside 696 external citations. Polarity classification is still indexing.
4
Pith papers citing it
696
external citations · OpenAlex
years
2026 4representative citing papers
Multi-agent LLM system with consensus and hierarchy outperforms individual models on disinformation detection tasks across English, Polish, Slovak, and Bulgarian datasets.
Zero-shot LLMs achieve near-chance ROC-AUC (~0.5) on ECG image classification while CNN models reach 0.92-0.94 internally and 0.85-0.86 externally on PTB-XL.
A university course design enables non-technical students across majors to reach the Create level of Bloom's taxonomy by repeatedly applying a problem-data-model-evaluation-reflection pipeline with concurrent ethics training and hands-on studios.
citing papers explorer
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Plainbook: Data Science, in Plain Language
Plainbook makes data-science notebooks natural-language-first by preserving cell descriptions, generating code via AI, enforcing linear execution via a checkpointing kernel, and adding value-centered cell and global tests.
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Multi-Agentic System Leveraging Open-Source LLMs to Mitigate Disinformation Threats
Multi-agent LLM system with consensus and hierarchy outperforms individual models on disinformation detection tasks across English, Polish, Slovak, and Bulgarian datasets.
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Physiology-Aware CNN and Zero-Shot Multimodal LLMs for ECG Image Classification: A Comparative Study
Zero-shot LLMs achieve near-chance ROC-AUC (~0.5) on ECG image classification while CNN models reach 0.92-0.94 internally and 0.85-0.86 externally on PTB-XL.
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From Understanding to Creation: A Prerequisite-Free AI Literacy Course with Technical Depth Across Majors
A university course design enables non-technical students across majors to reach the Create level of Bloom's taxonomy by repeatedly applying a problem-data-model-evaluation-reflection pipeline with concurrent ethics training and hands-on studios.