Challenges in cross-linguistic corpus-assisted discourse studies
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
In this paper, I present some of the challenges and benefits arising from the use of cross-linguistic (i.e., involving comparable, non-parallel corpora of different languages) corpus-assisted discourse studies. Since corpus linguistics and discourse analysis ultimately focus on ‘real’ language use rather than theoretically constructed examples, it follows that the content of a corpus will be as varied as the population it is intended to represent; and this is true to an even larger extent when the population is ethno-linguistically diverse. Data for corpus-assisted discourse studies (CADS) research, then, can present numerous issues to researchers, particularly if they are drawing on multilingual data. In this paper, four examples of cross-linguistic CADS challenges are drawn from two cases in Canada, a country that contains a diverse population that is indexed by two official languages, English and French. I conclude this paper by suggesting solutions for each of these issues and call for more research into the comparative nature of cross-linguistic CADS research.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it