Public policy, language practice and language policy beyond compulsory education: Higher education policy and student experience*
Bibliographic record
Abstract
Using higher education as a context, this article explores public policy and policy analysis in relation to language policy studies and argues for greater consideration of language issues in public policy and policy analysis. Conversely, language policy studies must also expand to integrate elements of public policy analysis in order to reveal the complexities of language practices and policies in societies where linguistic heterogeneity is the norm. This article is divided in two parts, with the first part drawing on a literature review to explore language issues in public policy for higher education. Using data from various studies on Francophone students’ access to and postsecondary experiences in a minority context, the second part will examine higher education in Ontario, Canada, from a public policy and a language policy perspective.*The author wishes to thank the reviewers for their helpful comments, the participants of the 2010 Language Policy and Planning Invited Symposium for the dialogism of our first meeting, and Professor Emeritus Stacy Churchill for his mentorship, his stewardship to the field of LPP and his inspiring work.
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.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".