Three Papers in the Applied Use of Machine Learning and Artificial Intelligence Models for the Analysis of Political Text Data
Bibliographic record
Abstract
This dissertation advances the frontier of computational political science by developing novel AI-driven methodologies for analyzing large-scale political discourse. It addresses three in- terconnected challenges in legislative and deliberative democracy research: 1) scaling quali- tative measurements, 2) mapping complex argumentative structures, and 3) enhancing text classification efficiency. The first study introduces a prompt-engineering framework leveraging large language models (LLMs) to automate the coding of deliberative quality in parliamentary speeches. I show that through a combination of detailed code book-style annotation instructions and examples drawn from a pre-validated collection of speeches, LLMs can achieve human-level performance when applying the Discourse Quality Index (DQI) to legislative debates from the US Congress. Building on this, the second study presents LegisGraph, a new approach combining LLMs with network science to represent legislative debates as structured argument graphs, where nodes represents speeches, speakers, arguments and topics, and edges capture relationships between them. Applying it to a representative corpus of Canadian parliamentary debates, I show how this method can be scaled to analyze large corpora of parliamentary debates, enabling analysis of a wide range of dynamics, including topic distribution, discourse quality trends, and patterns of polarization. The third study focuses on improving text classification efficiency by developing an algo- rithm that combines probabilistic modeling with active learning. By leveraging both labeled and unlabeled data, and focusing labeling efforts on challenging documents, this approach significantly reduces the need for labeled data while maintaining high classification accuracy. I demonstrate the effectiveness of this method through replication of two published studies with only a fraction of the original labeled data. Collectively, these studies demonstrate the transformative potential of AI in political com- munication research, offering scholars powerful tools to analyze vast corpora of political text with unprecedented depth and efficiency. This work lays the groundwork for new research that can shed light on the complexities of legislative and deliberative processes, informing policy-making and democratic governance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".