Equivocation in Political Discourse: How Do We Know When It Is Happening?
Bibliographic record
Abstract
Equivocation is a mode of speech adopted when the speaker wishes to avoid a direct answer to a question but is unwilling to resort to telling a lie. The result is a mode of speech at least partly designed to obscure communication rather than achieve clarity. However, determining what is equivocation is complex, not least as it can show attributes close to that expected when making a nuanced argument that takes account of lack of information or current controversies about the issue. This suggests the equivocation is much a product of how a given speech is interpreted by the observer. This study takes an interview given by President Trump shortly after his inauguration in 2016. Each block of speech is coded for evidence of equivocation (and, if so, of the type of equivocation). The main finding was that he systematically used equivocation in the interview with the manner of this shifting a little as the focus of the interview changed. However, there was no correlation between his use of equivocation and those sections were other observers have suggested his answers were actually dishonest. The extent to which he uses equivocation as a normal part of speech calls into account earlier assumptions that politicians resort to this mode of discourse at particular times rather than as a standard response. In turn, this requires the observer to consider how they define equivocation and how they respond when a particular politician uses this approach.
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How this classification was reachedexpand
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".