Bibliographic record
Abstract
Medical writings are generally standardized in language and concentrated on highly technical terms, but it can be difficult to understand due to its many forms and complexity as well. This paper adopts Hallidayan Functional Grammar to analyze nominalization in EMP (English for Medical Purpose) and the role played by it. With a corpus of Discussion Sections of 10 authentic medical papers by native English writers and 10 by Chinese academic writers drawn from very influential medical journals, the author has carried out a formal comparative analysis of three aspects: frequency of nominalization, lexical density. Firstly, what the author has attempted in this paper is to convince the readers that nominalization is a most powerful device in English by touching upon the relevant aspects with regard to nominalization: its wide range of morphological types as well as its typical functions in constructing EMP. Nominalization makes the whole text a solid block of information. Hence, the messages transferred by EMP allow little doubt or argument. It is shown that the roles played by nominalization are in accordance with the special requirements of EMP. Secondly, by identifying, analyzing and interpreting the nominalization in medical papers written by native English writers and Chinese writers from three aspects: the frequency of nominalization, lexical density and thematic progression, it has been found that nominalization accounts for the higher percentage for native writers, which serve to organize texts and might be the reason for their fluency and coherence. This paper suggests that nominalization plays a crucial role in building the logical structure of medical English papers and improving its formality. The results of the analysis show that Chinese writers have significantly insufficient use of nominalization in their medical papers. Accordingly, in teaching English academic writing to Chinese, attention should be paid to the application of nominalization. The author hopes that this paper will yield some insights and contribute to the studies of grammatical metaphor and the teaching of writing medical papers in China. Key words : Grammatical metaphor; Nominalization; English for Medical Purpose
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".