The Persuasive Power of Hedges: Insights from TED Talks
Bibliographic record
Abstract
The corpus-based study focuses on the use of hedges in persuasive TED Talk speeches, which are powerful, premeditated speeches delivered in a distinctive communicative environment that combines elements of both spoken and written discourse. The authors employ both quantitative and qualitative methods to analyze the hedging devices used to bolster the three rhetorical appeals: ethos, pathos, and logos. The results show that only 2% of the words in the corpus serve as hedging devices, which is lower compared to previous studies on written and spoken discourse. The incidence of hedges is highest in the logos parts, followed by pathos, with the lowest incidence in ethos. Strong credibility is generally established by avoiding hedging devices. To evoke emotions in the audience, the speakers mainly rely on adverbs and verbs. The use of approximators and shields to strengthen logos resembles the use of hedges in written academic discourse. The qualitative analysis focuses on the four most commonly used hedges: ‘actually’, ‘just’, ‘could’, and ‘think’. ‘Actually’ has a mitigating effect when it promotes intimacy, indicates the speaker's commentary, or introduces a challenging, even reinforcing effect. ‘Just’ is often used to convey a mildly positive or reassuring tone in communication. Both the parenthetical phrase ‘I think’, used in a variety of meanings, and the modal verb ‘could’, used as a hypothetical possibility, most often enhance the logical strength of an argument. The paper suggests incorporating these findings into ESL teaching materials and conducting further studies on the topic, as most existing studies focus on developing a scientific argument in writing. Developing an argument in speech is distinct and deserves attention.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".