Bibliographic record
Abstract
Sexism has existed since ancient times, and it is also a long-standing topic in academia. Language itself has no emotional meaning, but as a carrier of culture, it will doubtlessly reflect the phenomenon, and English is no exception. There are many examples of sexism in English vocabulary. This paper studies the sexism in English vocabulary and the understanding of these expressions in Chinese context. First of all, as Chinese users, the author briefly introduces the concept of linguistic sexism. Then the author concretely studies the manifestations of sexism in English vocabulary when these expressions come to Chinese language users and learners; in the end the author comes up with some suggestions about the ways of correctly understanding the linguistic sexism from the above studies and materials in Chinese context. Through the study of the author finds that the existence of sexism is still an indisputable fact, and is still seen in vocabulary everywhere. This phenomenon is partly due to the deep-rootedness of the idea of male superiority. And this phenomenon can be easily understood by Chinese users and learners. As China is a male-dominated country people hold the view that women are inferior to men. So the understanding of Chinese users and learners give hints to the cultural inclination of the nation.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".