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Record W6997202741

Towards plurilingualism in Montreal French Schools: a critical discourse analysis of current governmental and school board policies

2018· dissertation· en· W6997202741 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCharterCritical discourse analysisDiscourse analysisImmigrationLegislationHigher educationFrenchThrivingEquity (law)
DOInot available

Abstract

fetched live from OpenAlex

Many studies demonstrate that plurilingual practices in an educational setting have positive advantages on the social and cognitive development of a child. Initiatives within Québec research fields demonstrate that paying attention to language awareness and plurilingualism can facilitate the learning of French. Nonetheless, it is rare for Montréal French public schools to encourage these approaches. The socio-historical evolution of the province demonstrates that after a continuous fight to get French recognized as the language of the public sphere, legislation was used as a tool to ensure the learning of French by newly arrived immigrants as well as to improve the quality of French language-use among all Quebecers. Today, the Charter of the French Language dictates measures that school boards must take to assure the vitality of the French language. These measures are articulated in language policies that are elaborated in each school board. This study seeks to identify and better understand the facilitators and barriers of plurilingualism. Through a Critical Discourse analysis of the discourse in governmental and the Commission Scolaire de Montréal policies, this study aims to better understand the gap between the equity and cultural valorization discourse in policy and the unilingual practice currently privileged in Québec schools. The research will be used to inform school educators about ways to support education for plurilingualism in current educational policy, encourage education stakeholders to engage in professional self-development about education for plurilingualism, and offer equitable recommendations.

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 imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.892

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.005
Science and technology studies0.0230.031
Scholarly communication0.0150.006
Open science0.0020.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.038
GPT teacher head0.424
Teacher spread0.385 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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