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.
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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 | 0.412 | 0.575 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.031 | 0.032 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.036 | 0.047 |
| Open science | 0.019 | 0.049 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".