From cross-linguistic to intersectional corpus-assisted discourse studies
Bibliographic record
Abstract
Ten years ago, I highlighted challenges arising from the application of CADS to multilingual datasets in an approach called “cross-linguistic corpus-assisted discourse studies” (Vessey, 2013). In the intervening years, the notions of superdiversity and translanguaging have been largely transformative in the fields of applied and sociolinguistics; research applying these notions has raised important questions about boundaries between languages and the nature of diversity in contemporary social contexts (e.g., Blommaert and Rampton, 2011). Drawing and building on these theoretical advances, in this paper I propose to resituate cross-linguistic CADS within a broader intersectional CADS framework (Candelas de la Ossa, 2019; Jaworska and Hunt, 2017; Hunt and Jaworska, 2019; Kitis, Milani and Levon, 2018; Subtirelu, 2015). Specifically, I underscore the methodological contributions that CADS research can make to the study of intersectionality (Nash, 2008) and I suggest how intersectional theories can support and enrich CADS researchers’ arguments about “non-obvious” meaning (Partington, 2017).
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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 | 0.054 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".