Bibliographic record
Abstract
Language attitudes studies are a central part of both sociolinguistics and social psychology, and Quebec, Canada’s only province with a francophone majority, has proved to be one of the most fascinating places for this kind of research. This thesis is an investigation of the attitudes that anglophone, francophone and allophone Quebecers – both immigrants as well as those who were born in the province – hold towards English, Quebec French and European French. The first part of the thesis provides the context for the author’s own research. It outlines the most relevant events in Quebec history and explains the current social and linguistic situation in the province. Furthermore, it provides an introduction to attitude theory in general and language attitudes in particular, before summarising the most significant previous investigations into language attitudes in Quebec. The second part of the thesis focuses on a language attitudes study conducted amongst 164 Montreal college students in the autumn of 2007. The study made use of two different methodologies: a direct method (questionnaire) and an indirect method (the so-called matched-guise technique). The results of each method of inquiry are first presented separately, before being compared and analysed in the light of the current social and linguistic situation in the province of Quebec.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".