Comparing across languages in corpus and discourse analysis: some issues and approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it