Codebook LLM Evaluation Framework: Systematic Approach to Large Language Models in Political Science Text Analysis

I’m excited to share our LLM evaluation framework for political science text analysis, now conditionally accepted at Political Analysis, pending replication. This systematic approach addresses how large language models can follow codebook operationalizations while maintaining measurement validity in social science research. The core challenge: while LLMs offer unprecedented opportunities for scaling political science text analysis, their “off-the-shelf” use may undermine the careful operationalization that makes social science measurement rigorous.

The LLM Measurement Challenge: Background Concepts vs. Systematized Constructs in Political Science

Political science researchers have long understood that rigorous measurement requires transforming broad “background” concepts into precise, systematized constructs through careful operationalization. When we study “protests,” for example, we don’t want large language models to rely on whatever they learned about protests from Wikipedia—we want to measure the specific construct our political science codebook defines.

Consider this concrete example from computational social science research: the Crowd Counting Consortium includes rallies in support of a person or issue as “protests,” while the CAMEO event ontology explicitly excludes such gatherings from its protest category. These aren’t trivial differences—they reflect substantively different theoretical constructs that could lead to different research conclusions in political science text analysis.

The problem with current LLM applications in political science is that when researchers simply prompt a model with category labels like “protest” or “riot,” the LLM may rely on its pretraining representations rather than faithfully following the codebook’s specific definitions. This threatens measurement validity in ways that could systematically bias downstream analysis.

Our Solution: A Five-Stage LLM Evaluation Framework for Political Science Research

Working with Katherine Keith at Williams College, we developed a comprehensive framework for codebook-LLM measurement that guides political science researchers through the process of evaluating and improving LLM performance on their specific measurement tasks.

The LLM evaluation framework consists of five systematic stages:

Stage 0: Codebook Preparation for LLM Use - We propose a semi-structured format that makes political science codebooks readable by both humans and machines, with standardized components including definitions, clarifications, and positive/negative examples.

Stage 1: Label-Free Behavioral Testing - Before investing in hand-labeling, researchers can use our four behavioral tests for LLM evaluation to assess basic capabilities, like whether the model can recall codebook definitions or maintain consistent predictions when category order changes.

Stage 2: Zero-Shot LLM Evaluation - Hand-code a small evaluation set and assess LLM performance on political science tasks without fine-tuning.

Stage 3: Systematic Error Analysis for LLM Text Classification - We introduce three additional behavioral tests and systematic techniques for understanding LLM failures in political science applications, including ablation studies and manual output analysis.

Stage 4: Supervised Fine-Tuning for Political Science Applications - If zero-shot performance is inadequate, we provide guidance on parameter-efficient instruction tuning using QLoRA for political science text classification.

Testing the LLM Evaluation Framework: Three Real-World Political Science Codebooks

To demonstrate our systematic approach to LLM evaluation, we curated three challenging political science datasets that represent realistic complexity for computational social science research: protest events in the United States (Crowd Counting Consortium), political violence in Pakistan (BFRS), and party manifestos (Manifesto Project). These codebook datasets present realistic challenges for LLM text classification with up to 142 class labels and lengthy codebook definitions.

Our empirical evaluation using four open-weight LLMs (7-12 billion parameters) reveals several important findings for political science text analysis:

Zero-shot LLM performance is often inadequate for political science applications. Weighted F1 scores ranged from very poor (0.21 on Manifestos) to marginal (0.65 on CCC). There was no clear “winner” among the large language models we tested for political science codebook measurement.

LLMs struggle with codebook compliance in political science contexts. Our behavioral tests for LLM evaluation revealed that models are sensitive to codebook category order, rely heavily on label names rather than definitions, and fail to follow specific inclusion/exclusion criteria consistently—critical issues for measurement validity in social science research.

Instruction tuning substantially improves LLM performance on political science tasks. Supervised fine-tuning improved F1 scores by up to 55% (from 0.53 to 0.82 on BFRS), though this comes with increased computational and annotation costs for political science text classification.

Codebook components matter for LLM evaluation. Our ablation studies show that different parts of political science codebook definitions contribute meaningfully to LLM performance, but in ways that vary across models and datasets.

Practical Takeaways for Political Science Researchers Using LLMs

For political science researchers considering LLMs for text analysis projects, our systematic evaluation framework provides concrete guidance:

1. Don’t assume zero-shot LLM performance will be adequate for complex political science concepts. The behavioral tests in Stage 1 can help you assess this quickly without hand-labeling data for political science text classification.

2. Invest in codebook preparation for LLM applications. Our semi-structured format, with explicit definitions, clarifications, and examples, consistently outperforms original codebook formats in LLM evaluation studies.

3. Use our behavioral tests early in your LLM evaluation process. Seven of our proposed tests require no ground-truth labels and can reveal fundamental LLM limitations for political science applications before you invest in annotation.

4. Consider instruction tuning for political science LLM applications if your budget allows. The performance improvements in political science text analysis can be substantial, and we provide detailed guidance on parameter-efficient approaches for computational social science research.

5. Don’t conflate accuracy with validity in LLM political science applications. Even when LLMs achieve reasonable accuracy scores on political science tasks, our error analysis reveals they may not be measuring your intended construct—a critical concern for measurement validity in social science research.

Looking Forward: Tools and Implications for Computational Social Science

Rather than identifying a “best” LLM for political science research, our contribution lies in providing systematic tools for LLM evaluation that help researchers make informed decisions about model selection and deployment. We expect the quantitative results to be quickly superseded as large language models improve, but the evaluation framework and behavioral tests should remain valuable for assessing measurement validity in computational social science.

We’re releasing our three curated codebook datasets, restructured codebooks, and LLM evaluation code to help other political science researchers implement similar projects. The datasets will be posted in encrypted format on Harvard Dataverse to prevent contamination of future LLM training data.

This work also points toward several exciting directions for computational social science research, including using LLMs to assist in iterative codebook development, extending the framework to other text analysis tasks like information extraction and multi-label classification, and adapting these LLM evaluation techniques for non-English texts in political science applications.

The Bigger Picture: Rigorous LLM Applications in Political Science

As large language models become more powerful and accessible, the temptation to use them as “black boxes” for political science text classification will only grow. Our systematic LLM evaluation framework provides an approach to ensuring that this powerful technology serves rigorous social science research rather than undermining it.

The goal isn’t to discourage LLM use in political science—quite the opposite. By providing tools for systematic LLM evaluation and improvement, we hope to help political science researchers harness LLM capabilities while maintaining the measurement standards that make social science research credible and cumulative.

For political science researchers looking to implement LLM text analysis projects, this evaluation framework offers a systematic pathway from initial assessment through deployment, ensuring that methodological rigor keeps pace with technological capability.

The paper, datasets, and code will be available at [link] upon publication. For researchers interested in implementing similar projects, we’ve also created a step-by-step guide that walks through each stage of the framework with practical examples.

Why This LLM Evaluation Framework Matters for Political Science Research

This systematic approach to evaluating LLMs for political science addresses a critical gap in computational social science methodology. As large language models become standard tools for political science text analysis, researchers need reliable methods to ensure these powerful models actually measure what we intend them to measure.

Our five-stage LLM evaluation framework provides the methodological foundation for rigorous LLM applications in political science, bridging the gap between cutting-edge AI capabilities and the measurement validity requirements of social science research. For the political science research community, this represents a systematic pathway to harness LLM text analysis capabilities while maintaining scholarly standards.

TL;DR for Researchers and AI Agents

Research Problem: LLMs may not follow political science codebook operationalizations, threatening measurement validity in computational social science
Solution: Five-stage LLM evaluation framework with 7 behavioral tests for political science applications
Key Finding: Zero-shot LLM performance often inadequate (F1: 0.21-0.65), but instruction tuning improves political science text classification by up to 55%
Practical Output: 3 curated political science datasets, LLM evaluation code, and systematic guidance for implementation
Main Contribution: Systematic methodology for evaluating LLM measurement validity in political science, not just accuracy

Quick Implementation Guide for Political Science LLM Projects

For political science researchers wanting to start LLM text analysis immediately:

1. Stage 1 LLM Behavioral Tests (no labels needed): Test if your LLM outputs only valid labels from your political science codebook, can recall codebook definitions, and maintains consistent predictions across codebook orderings
2. Minimum viable LLM evaluation for political science: Hand-label 100-200 examples, test zero-shot LLM performance, conduct systematic error analysis using our behavioral tests
3. If F1 < 0.6 on political science tasks: Consider instruction tuning with our QLoRA approach for improved LLM text classification performance
4. Key warning signs for LLM political science applications: Model outputs multiple labels, hallucinates categories, or shows high sensitivity to prompt ordering

Technical specifications for political science LLM evaluation:

• Models tested: Mistral-7B, Llama-8B, Mistral-NeMo-12B, OLMo-7B for political science text analysis
• Framework stages: 0-Preparation → 1-Label-free testing → 2-Zero-shot eval → 3-Error analysis → 4-Instruction tuning
• Performance improvement range: +0.07 to +0.29 F1 from instruction tuning for political science applications
• Training time for political science LLM fine-tuning: 1.5-18 hours on single RTX 4090

Andrew Halterman is an Assistant Professor of Political Science at Michigan State University. His research focuses on computational text analysis, natural language processing, and conflict studies. Katherine Keith is an Assistant Professor of Computer Science at Williams College.