Comparing across languages in corpus and discourse analysis: some issues and approaches
Bibliographic record
Abstract
Corpus-assisted discourse studies is, by its nature, interdisciplinary. However, this need to reach across borders becomes even more salient when we study discourses across languages, and this represents a natural intersection with translation studies. The aim of this paper is to reflect on the issue of comparison in cross-linguistic corpus-assisted discourse studies, positing a series of key questions including: How do we compare across or within corpora containing different languages? How do we identify meaningful language units for comparison in this context? How do we know that we are comparing like with like? Using a series of case studies, we start by addressing how we can approach comparison at the lexical level. We then move on to consider methods which allow us to abstract above the lexical level using three case studies which illustrate the use of semantic fields, discourse frames and rhetorical features. By presenting some issues and partial solutions regarding comparison across and within multilingual corpora, we hope to initiate a productive discussion in which we will also be able to collectively enrich and inform this set of resources.
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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 | 0.230 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.035 | 0.038 |
| Science and technology studies | 0.017 | 0.049 |
| Scholarly communication | 0.042 | 0.048 |
| Open science | 0.010 | 0.027 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".