Political Disaffection and the Decline of the Centre: Quantitative Text Analysis Approaches
Bibliographic record
Abstract
Over many decades of research on anti-establishment politicians, scholars have made claims about how they speak. These descriptions are typically critical in nature: populists present the world in terms of moral binaries; they invoke negative emotions like anger and fear; they express emotion intensely and frequently; they are simple and short in speech; they break politeness norms. Such claims are often taken as commonplace wisdom. Yet, systematic measurement of whether these descriptions are true for fringe politicians, more than for their conventional counterparts, is surprisingly sparse. There is reason to believe that anti-establishment figures and politicians of the mainstream may not be as clearly distinguishable in speech as is sometimes thought. Political speech in general has perhaps become simpler since mid-century; mainstream parties are known to borrow stylistic traits and policy positions from rising challengers; and observers have pointed to anxiety-invoking cross-party campaigns in the past decade in Britain. In response, this dissertation applies relatively novel tools for processing large amounts of unstructured data. Text data from United Kingdom and Canadian lower house parliamentary transcripts (1990-2022; 1988-2022) and audio signal data are the basis for tests to assess whether claims made about anti-establishment political figures’ speech are correct.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".