Affix Acquisition of Chinese English Learners: A Case Study Based on a Self-Built Corpus
Bibliographic record
Abstract
Nowadays, theoretic and empirical research into affix acquisition in Second Language Acquisition has attracted increasing attention (Peng Tingting, 2009; Zhao Ming, 2014; Chen Jie, 2017). However, there are still few empirical studies on affix acquisition of Chinese English learners, especially from the perspective of corpus linguistics. The present research aims to find out how affixes are acquired and used in written texts by Chinese English learners. A case study was conducted based on a self-built corpus. All of the data are collected from 174 undergraduate students majoring in English at a university in central China. Compleat Lexical Tutor (v.8.3) and AntConc (v.3.5.2) are used to process and analyze the data. As is shown in the results of the research, affixes acquired and used by Chinese English learners can be divided mainly into the following categories: 1. High-frequency affixes, such as -s, -ed, -ing, etc. 2. Intermediate-frequency affixes, such as -ly, -al, etc. 3. Low-frequency affixes, such as im-, in-, ir-, etc. Therefore, the affixes that are used most frequently are -s and -es, but prefixes are seldom used in written text. The present study is beneficial for providing a crucial reference for the instruction of vocabulary and writing in colleges.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".