Bibliographic record
Abstract
This paper (Note 1) examines the form sort of in British men and women’s speech, and investigates whether there is a gender difference in the use of this form. We do so through corpus analysis of the British National Corpus (BNC). We contend there is no quantitative difference in the use of sort of in men and women’s speech. Contrary to general belief, we claim women do not use hedges more than men. However, there is a functional difference. Men and women use the item sort of for slightly different functions: in particular, women use these forms as politeness devices more often than men. The results of this study do not present any evidence to support the widespread claim that women use the forms sort of more than men do. There is no significant gender difference in the frequency of sort of. In the light of these results, women’s allegedly greater usage of these forms could be regarded as persistent folklore. Furthermore, sort of has been treated mainly as a sign of powerlessness and uncertainty. Yet, this study indicates that it is a useful interpersonal resource, which both men and women seem to appreciate. When examining the affective function in greater detail, some marked gender differences emerge. The main finding is that positive affect is more common in women’s speech, whereas negative affect is clearly more frequent in men’s speech. In much of the research on gender differentiated conversational strategies, women’s language is presented as co-operative and men’s language as competitive. The results of this study show that, with respect to sort of, such a distinction cannot be drawn. Even if negative affect is more common in men’s than in women’s speech, by far the most instances of interpersonal function occur in essentially co-operative behaviour not only in women’s but also in men’s speech. This notion does not fit the description of men’s speech style being competitive and prone to conflict. Hence, the results of this study suggest that men use the marker sort of more co-operatively than many researchers allow for.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".