How the monitored publications were assessed

Discourse valence across every scored item: what the sampled sources published and how independent models read it. It is not a measure of public opinion, not a measure of the state of the world, and article volume is not agreement.

7 items read as adverse

• AI’s mathematics blitz pushes science into strange new territory
  -57 The Economist: Science and Technology 2026-10-12 low agreement · ±100
• Will AI grant reviews make science more conservative?
  -50 Nature 5d ago single model
• What Does an Ontology Catch? Curated, LLM-Generated and Data-Derived Ontologies as Checkers of LLM Agents over Knowledge Graphs
  -50 arXiv cs.AI 8d ago single model
• AI-hallucinated citations should be treated as research misconduct
  -50 Nature 12d ago single model
• British households would be £5,000 richer if we were more innovative. Let’s unleash our talents | Gordon Brown
  -33 The Guardian: Artificial Intelligence 2026-10-09 low agreement · ±170
• Why and How to Do Science When AI Is Better at It: A Framework Grounded in Human Understanding and Direction
  -26 arXiv cs.AI 11d ago low agreement · ±150
• Matched but Not Better: An Equivalence-Tested Benchmark of Quantum Machine Learning on Six Clinical Datasets and the QML-Health-READY Reporting Framework
  -25 arXiv cs.LG (Machine Learning) 3d ago single model

Across the whole window

The two tables below describe every item in the last 30 days under science. They are deliberately not narrowed by the reading filter (currently adverse), because a mean taken only over items already selected for their score would just restate the filter.

By topic

By source

A source's mean says how the items it published were read. It is not a rating of the source.