·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Mathematicians want proof OpenAI didn’t use their work3h◆Powering AI is an architecture problem3h◆Planning and Scheduling Business Processes under Control-Flow Uncertainty10h◆A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems10h◆Risk Is Not Review Value: Wrong-Answer Exposure Under Bounded Review Budgets10h◆From Simulated Citizens to Simulated Deliberation: Challenges in Representation and Interaction10h◆FinCUABuild: Can Agents Build Reliable Benchmarks for Dynamic Financial Computer Use?10h◆AgentIdeaBench: Benchmarking Scientific Ideation in the Agent Era10h◆Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best10h◆A radiographic world model for clinical reasoning and evidence generation10h◆The Profit Alignment Problem: How Profit Mandates Induce Alignment Failures in LLMs10h◆When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems10h◆Do Large Language Models Know What They Don't Know II? A Fully Behavioral, Non-Cognitive Measure of Epistemic Honesty10h◆Explainable Temporal Attention-based Defect Detection For Fillet Joints in Real-Time Gas Metal Arc Welding Based on Multi-modal Data10h◆Quantization Amplifies Determinism, Not Bias: Scale-Dependent Behavioral Effects of Serving-Time Weight Compression10h◆PRIMUS: Identity, Governance, and Verification for Multi-Agent Federations10h◆Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails10h◆A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes10h◆Robots Influencing Humans to Reveal their Goals during Collaboration and Competition10h◆An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling10h◆Mathematicians want proof OpenAI didn’t use their work3h◆Powering AI is an architecture problem3h◆Planning and Scheduling Business Processes under Control-Flow Uncertainty10h◆A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems10h◆Risk Is Not Review Value: Wrong-Answer Exposure Under Bounded Review Budgets10h◆From Simulated Citizens to Simulated Deliberation: Challenges in Representation and Interaction10h◆FinCUABuild: Can Agents Build Reliable Benchmarks for Dynamic Financial Computer Use?10h◆AgentIdeaBench: Benchmarking Scientific Ideation in the Agent Era10h◆Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best10h◆A radiographic world model for clinical reasoning and evidence generation10h◆The Profit Alignment Problem: How Profit Mandates Induce Alignment Failures in LLMs10h◆When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems10h◆Do Large Language Models Know What They Don't Know II? A Fully Behavioral, Non-Cognitive Measure of Epistemic Honesty10h◆Explainable Temporal Attention-based Defect Detection For Fillet Joints in Real-Time Gas Metal Arc Welding Based on Multi-modal Data10h◆Quantization Amplifies Determinism, Not Bias: Scale-Dependent Behavioral Effects of Serving-Time Weight Compression10h◆PRIMUS: Identity, Governance, and Verification for Multi-Agent Federations10h◆Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails10h◆A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes10h◆Robots Influencing Humans to Reveal their Goals during Collaboration and Competition10h◆An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling10h◆
News/Structured Disagreement in Health-Literacy Annotation: Epistemic Stability, Conceptual Difficulty, and Agreement-Stratified Inference
arxiv
PublishedApril 23, 2026 at 4:00 AM
—neutral

Structured Disagreement in Health-Literacy Annotation: Epistemic Stability, Conceptual Difficulty, and Agreement-Stratified Inference

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.19943v1 Announce Type: new Abstract: Annotation pipelines in Natural Language Processing (NLP) commonly assume a single latent ground truth per instance and resolve disagreement through label aggregation. Perspectivist approaches challenge this view by treating disagreement as potentially

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#nlp#annotation#health-literacy#perspectivist

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#nlp#annotation#health-literacy#perspectivist

Related coverage

More from ARXIV
arxivPlanning and Scheduling Business Processes under Control-Flow Uncertainty10harxivA Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems10harxivRisk Is Not Review Value: Wrong-Answer Exposure Under Bounded Review Budgets10harxivFrom Simulated Citizens to Simulated Deliberation: Challenges in Representation and Interaction10h
The Bubble Brief
WEEKLY

Read nlp insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews