Automatic Annotation of Semantic Fields for Political Science Research
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT This article discusses methods for automatic annotation of political texts for semantic fields—groups of words with related meanings. This type of annotation is useful when studying political communication, such as legislative debate or political speeches. We present three types of automatic annotation: unsupervised clustering, dictionary-based approaches, and a method based on relevant experimental data. All methods are applied to analyzing Margaret Thatcher's political rhetoric. For this data, we find that unsupervised clustering is most useful for tracing topics; dictionary-based methods are most effective in a comparative setting; whereas the last method is the most promising for detecting off-topic, singular uses of semantic domains, which are often rhetorical tools used to achieve a political end. Applicability, strengths, and weaknesses of each method and of their combinations are addressed in detail.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it