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.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".