Montreal's linguistic landscape: Instances of top-down and bottom-up language planning
Bibliographic record
Abstract
In Montreal, federal bilingualism, provincial monolingualism, and municipal realities of widespread bilingualism have all left a deep impression on the linguistic landscape of the city. Legislation of the languages on public signs was enacted in 1977, with a view to unambiguously project a visage français (Levine 1989) of Montreal, a projection aimed, in no small part, to immigrants considering which language to shift toward. Initially requiring all outdoor signage to be monolingually French, the Charter of the French language now mandates French to be present and «markedly predominant» if accompanied by other languages. Top-down legislation regulating the linguistic landscape (LL) comes from both provincial and federal sources. However, bottom-up (Ben-Rafael et al 2006) policies embraced by a variety of stakeholders (com- munity groups, individual businesses, private persons) also leave visible traces in the LL, and the way languages are used in these manifestations interacts in interesting ways with the legislation. Considering language choices in the LL emanating from the «grassroots», and bearing in mind that these may have the potential to redress power inequalities (Tollefson 2013), this chapter presents examples found in Montreal’s LL that give visibility to the city’s multiple languages, thus claiming their legitimacy. The resulting LL, notwithstanding the huge diversity of languages and the important mediating role of English, remains, for the most part, «markedly predominantly» French.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.026 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".