An Empirical Study on the Projection of Specificity in the Usage of Modifiers in Chinese College EFL Writing
Bibliographic record
Abstract
Specificity, as a dimension of cognitive construal, refers to the capacity of a speaker to describe an entity or a situation in different accuracy and details (Langacker, 2008), which is linguistically reflected in lexical and grammatical levels (Wen, 2012). Modifiers can extend a simple sentence into a long and complicated one (Weng, 2007), indicating how accurately and substantially an entity or a situation is depicted by a writer. Cognitive linguistics holds the concept that thoughts can be reflected in language (Zhang, 2007) and accordingly the choice of lexical terms and grammatical structures can project the writers’ intention and preference (Wen, 2012). Given the projective significance of grammatical structures, the study on the usage of modifiers in EFL writings can demonstrate the projection of specificity and investigate the writers’ cognitive activity during writing. Although numerous studies about EFL wring have been done from the perspective of cognitive linguistics in the past decades, research aiming at the cognitive process during writing has been far from satisfaction. Hence, this study will analyze the usage of modifiers and how it projects specificity in specific writing. 20 writing papers are randomly selected as samples written by the second-year college EFL Learners from Leshan Normal University, Leshan, China. Data about the usage of modifiers in each sample are collected by dividing each sentence into smaller unit of modifiers centering on nouns and verbs, ensuring the specificity of each sample to be analyzed in a quantitative level. The study concludes that the writers are inclined to apply modifiers for specificity to a certain extent but the lack of modifier diversity and the partial choice of familiar modifiers indicate monotonous descriptions and personal preference to project certain aspects of an entity or a situation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".