Borrowing of discourse functions of English "suggest" by Spanish "sugerir" in biomedical research articles: a contrastive study
Bibliographic record
Abstract
Corpus-based studies have grown in popularity as advances in computer technology have made it possible to analyse extremely large quantities of electronically stored text data. The application of corpus techniques to bilingual data in contrastive studies is a valuable method to gain insight into both grammatical patterns and the use of polysemous or multifunctional lexical items, allowing distinctions to be made between their meanings and functions. This has led to a plethora of English-Spanish studies both from a general language perspective (Labrador 2004, 2007; Rabadan 2006, 2007; Ramon-Garcia 2006, 2007) and in specialised domains of language such as medical discourse (Lopez-Arroyo and Mendez-Cendon 2007; Williams 2004, 2005, 2007, 2008). In biomedical research articles (RAs), the polysemous verb suggest is commonly used by authors as a hedging device to express tentative claims and to attenuate evaluation of other researchers’ work. In a wider context of academic prose, Biber et al. (1999) classify suggest as a communicative verb, and note that it occurs at a frequency of over 400 tokens per million words, is associated with a nominal that clause in over 100 cases per million words, and “when such [communication] activities are reported, they are often attributed to some inanimate entity as subject of the verb” (Biber et al. 1999: 372). Suggest, therefore, makes a considerable contribution to the impersonal style of both academic and scientific prose, and forms part of a cluster of verbs (including indicate, find, show, prove, demonstrate) that allow writers both to express their evidence-based claims along a scale of certainty
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".