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
This paper outlines how corpus linguistics—and more specifically the corpus-assisted discourse studies approach—can add useful dimensions to studies of language ideology. First, it is argued that the identification of words of high, low, and statistically significant frequency can help in the identification and exploration of language ideologies within corpora. The frequency of linguistic patterns and discursive representations may reveal trends in explicit representations of languages (i.e. metalanguage) and elisions where assumptions are made about the role of languages (i.e. implicit language ideologies). Secondly, collocation data can aid researchers in gaining greater insight into the ways in which languages are being represented (or not) within sites identified through frequency and statistical significance. Finally, the use of dispersion plots can help researchers to identify sites with high- and low-frequency items for closer analysis. The paper concludes with some of the limitations of the corpus linguistic approach in studying language ideologies. Examples are drawn from a larger comparative study of French and English language ideologies in corpora of Canadian newspapers.
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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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