Effectiveness of Corpus in Distinguishing Two Near-Synonymous Verbs: Damage and Destroy
Why this work is in the frame
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Bibliographic record
Abstract
This study aims to explore how corpus-based approaches can be used to address the distinctions of English near-synonyms effectively. Especially, it collected source data from the British National Corpus (BNC) and adopted Sketch Engine (SkE) as an analyzing tool to compare the near synonymous pair damage and destroy commonly misused by Chinese-speaking learners of English in terms of frequencies, genre distribution, colligation and collocation, differences in meanings and uses. It is found that damage and destroy are near-synonyms because they are relevant words and share most collocates but they are not fully intersubstitutable for certain contexts. Some words related to the human body or physical health are more collocated with damage and some such as military affairs and one’s thought or belief more with destroy. In addition, the core meaning of damage gives more emphasis on something that can be recovered but does not work well as before, while destroy offers more senses for something that no longer exists. Furthermore, the British tend to collocate the two near-synonyms with the same word to create a build-up, because destroy is endowed with a stronger degree of destruction than damage. The study ends by suggesting corpus-based analysis should be promoted in language teaching and learning to improve the accurate use of English vocabulary by language learners.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it