How to Become a Patent Translator: Tricks and Tips – Notions of Text Genre and Ceremony to the Rescue
Bibliographic record
Abstract
Before starting to translate patents and patent abstracts, translators – who are not discipline experts – need to be aware of the context of text genre production and the communicative purposes and intentions to make sure they are conveying the intended meaning in the accepted manner (of the typical ceremony) in order not to shock the recipient and distort the author’s original intent. This article aims to present the results of a study that analyzes both the moves of original patent abstracts in four languages (Chinese, Spanish, French and English) and four disciplines (medicine, chemistry, telecommunications, and IT) and the rhetorical value of linguistic choices (i.e., modality, verb tense, passive voice, adverbs and adjectives). The methodology used is twofold: qualitative so as to explain the text production context according to certain parameters related to text genre and give a better interpretation to the quantitative results, and quantitative so as to conduct a linguistic analysis of the patent abstracts. Text samples were chosen according to date of publication, original language and discipline, and form a corpus of 200 texts. The results appear to show that discipline does not play a major role in the linguistic choices made by the abstractors, and that boosting and hedging are both rhetorical ways to combine private intentions and collective purposes, while satisfying institutional requirements and recipients’ expectations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".