---
title: "A Corpus-based Gender Study of Hedges in Spoken British English"
description: "The paper asks whether hedging in everyday British speech is really a \"female\" trait, as Lakoff and later authors claimed. 20 references."
authors: ["Ruofan Du"]
year: 2021
venue: "Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation"
source: "https://aclanthology.org/2021.paclic-1.59/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/du-2021-corpus-based-gender-study-hedges/"
---
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# A Corpus-based Gender Study of Hedges in Spoken British English

**Authors:** Ruofan Du · **Year:** 2021 · **Venue:** [Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation](https://md.research.elamur.ai/free/en/venues/paclic/) ([PACLIC 2021](https://md.research.elamur.ai/free/en/venues/paclic/2021/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.paclic-1.59/) · [PDF](https://aclanthology.org/2021.paclic-1.59.pdf)

## Summary
The paper asks whether hedging in everyday British speech is really a "female" trait, as Lakoff and later authors claimed. It also asks whether hedging tracks social class. The author used the spoken part of the BNC and Prince et al.'s (1982) taxonomy, which splits hedges into approximators (adaptors, rounders) and shields (plausibility, attribution). From this, 35 frequent hedge expressions were chosen as search items. Frequencies were normalized per million words and compared with chi-square tests, overall and within each BNC social class (AB, C1, C2, DE). Concordance lines were then read to find out what the hedges actually do.

Overall, women hedge slightly but significantly more than men, and use of hedges increases with social class. However, each hedge type shows its own pattern: men lead on rounders and attribution shields. The author therefore argues that hedging is a general interpersonal resource, not a gender or class marker. The study matters as a corpus check on a long-repeated sociolinguistic claim.

## Key points
- Data: spoken texts of the BNC, using its speaker metadata for gender and social class (AB, C1, C2, DE).
- Inventory of 35 hedges:
  - Approximators: 10 adaptors (e.g. *sort of*, *quite*, *really*) and 8 rounders (e.g. *roughly*, *or so*, *at least*).
  - Shields: 12 plausibility shields (e.g. *I think*, *probably*, *might*) and 5 attribution shields (e.g. *it seems*, *they say*).
- Overall normalized frequency was about 32,510 per million words for females and 31,377 for males (χ² = 20.75, p \< 0.001). Significance was judged at χ² > 3.841 and p \< 0.05.
- The female lead holds in classes AB, C1 and C2, but disappears in DE (χ² = 0.92, p = 0.337). For both sexes, AB speakers hedge most and DE speakers least.
- By type, females use more adaptors (≈10,597 vs ≈9,925 per million, χ² = 22.23) and more plausibility shields. Males use more rounders and attribution shields.
- Qualitative reading of concordances found four functions of hedges:
  - signalling doubt;
  - protecting the listener's feelings;
  - searching for a word;
  - avoiding an expert pose.

## Interesting ideas
- Prince et al.'s split separates hedges that change a claim's truth value (approximators) from hedges that only show the speaker's stance toward it (shields), which is a clean axis for modelling commitment.
- An overall difference that is significant can vanish inside one stratum, as the gender gap does for DE speakers, so stratified re-testing shows whether an effect is general.
- The counted items include very ambiguous words (*about*, *can*, *very*, *always*), so raw string counts mix hedging and non-hedging uses; the paper's manual concordance reading partly compensates for this.
- Reading hedges in context yields functional labels (doubt, face-saving, word search, modesty) beyond the formal category of the expression.
- Attribution shields such as *they say* or *according to* pass responsibility for a claim to someone else.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (frames): prag could attach a commitment feature to each frame, marking rounders as "fuzzy value" and shields as "speaker stance" with their source (self vs. third party for attribution shields).
- **I2** (learn): evaluation should report parser or reader gains per genre or source slice, treating a slice where the effect disappears as a built-in negative control.
- **I3** (tree): the UD tree can tell *about* as a numeric modifier (advmod on a nummod) from *about* as a preposition (case), so hedge detection can work on structure rather than surface strings.
- **I4** (frames): these four functions could become a small label set on speech acts, helping implicature reasoning tell whether *I think* signals uncertainty or politeness.
- **I5** (world): facts introduced under an attribution shield could be stored as reported beliefs of a source rather than as world facts, keeping the world model honest and every answer explainable.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Ruofan Du. 2021. A Corpus-based Gender Study of Hedges in Spoken British English. Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{du2021corpus,
  title = {{A Corpus-based Gender Study of Hedges in Spoken British English}},
  author = {Ruofan Du},
  booktitle = {Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation},
  year = {2021},
  url = {https://aclanthology.org/2021.paclic-1.59/}
}
```

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## References

20 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*