A Survey of the Mixed Use of He/She for Chinese Freshmen in English Majors
Bibliographic record
Abstract
The mixed use of he/she in oral English is a hot topic in the field of second language acquisition in the past decades and the related study is numerous. However, there are few studies on the misuse of the two pronouns in oral English for English majors in Chinese universities. This study aims to explore the current situation and characteristics of he/she misuse in oral English of Chinese freshmen majoring in English and analyze the factors that cause the misuse, so as to arouse the learners’ awareness of the error. This study focuses on two research questions: (1) What are the characteristics of he/she misuse in spoken English of Chinese freshmen majoring in English? (2) What are the reasons for the misuse? Based on the selected spoken language materials, this study explores the mixing types and error rate of he/she and analyzes the characteristics of the misuse through data. Through the questionnaire, this thesis study investigates the causes of the misuse. It is found that the mixed use of pronoun he/she is serious in the spoken language of English major freshmen, especially the misuse of “she” into “he”. The factors that cause the misuse are complex, involving mother tongue, attention distribution and working memory, pronunciation relationship, transfer of training, and social environment and personal factors, among which the analysis from articulatory phonetics deserves more 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".