A Corpus-Based View Of Lexical Gender In The Inner Circle Countries Of The English Language
Bibliographic record
Abstract
For many centuries, gender becomes a never-ending topic to be discussed due to its impact towards society. Gender differences may occur by means of language which is the basis of people’s communication. In English language, the gender differences can be detected from lexical gender. Since English is an International language, it is necessary to see the Inner Circle countries by Kachru. The Inner Circle countries refer to where English is originally used and developed in which consists of five countries: the United States, Canada, Great Britain, Australia and New Zealand. Due to the similarity of their first-language, it is important to see one of gender terminological distinctions by Hellinger and Bussman i.e. lexical gender in which can be found in the use of forms of addresses and the use of professional titles. This study is a mixed-method of textual based study. This study uses corpus linguistics as the research approach and glowbe as the corpus tool to gather the big amount of data. The result shows that the most frequent form of address in the Inner Circle is the word Mr. It occurs not only because men only have one marital status but also people nowadays live in the patriarchal society. Meanwhile, the most frequent professional titles for male refer to the high status occupational titles i.e. Professor and Doctor. Men have greater figures in all countries in which it indicates that men are above women. For the most frequent female professional titles refers to the low status occupational titles i.e. in the word Nurse in which indicates that women are more likely to be placed beneath men and more associated with a job that is more into emotional feeling related. From the findings that have been obtained, it is now crystal clear that the most gender bias country is United States in which it can be seen from the results of the difference of both lexical gender terms. This case means that the society of United States still tend to side onto male party.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".