From cross-linguistic to intersectional 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
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).
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 | 0.001 | 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