Automated Detection and Structuring of Social Tipping Point Evidence in Climate related Documents: A Modular AI Framework

• URL: http://arxiv.org/abs/2609.12254v1
• Date: Thu, 10 Sep 2026 22:21:16 GMT
• Title: Automated Detection and Structuring of Social Tipping Point Evidence in Climate related Documents: A Modular AI Framework
• Abstract summary: This paper presents an open and modular transformer-based framework that detects and structures social tipping point evidence at the passage level.<n>The framework joins five components into a single deployable workflow: a DistilBERT boundary splitter for segmentation, an iteratively augmented RoBERTa classifier for detection, a Mistral 7B model that rewrites each detected passage for clarity, and a LLaMA 3.2 3B model that rates the passage against five published social tipping point criteria.
• Score: 2.157504175521934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The climate literature has grown faster than review teams can read it. That gap matters most for a concept like the environmental social tipping point, the threshold at which a small change triggers rapid, self-reinforcing change in a social system. Evidence of this kind of shift is usually contained in one or two paragraphs within a longer document. As a result, existing text mining tools-which categorize entire documents by topic or highlight isolated claims-leave an expanding set of important evidence without any systematic method for discovery or organization. This paper presents an open and modular transformer-based framework that detects and structures social tipping point evidence at the passage level. The framework joins five components into a single deployable workflow: a DistilBERT boundary splitter for segmentation, an iteratively augmented RoBERTa classifier for detection, a Mistral 7B model that rewrites each detected passage for clarity, a LLaMA 3.2 3B model that rates the passage against five published social tipping point criteria, and a Milvus vector store for semantic retrieval. The system is wrapped in a Streamlit interface backed by MinIO object storage. Evaluated on a 163-passage benchmark labelled by GPT-4.1 and a 51-passage set reviewed by experts, the splitter surpassed three competing methods on a nine-metric composite score (6.137). The tuned RoBERTa model achieved 71.4 percent accuracy with a Cohen's kappa of 0.337 on the full benchmark, and 87.5 percent accuracy with a kappa of 0.742 on passages with labels, outperforming both a climate-focused model and untuned language models.

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