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Record W2558214299

Montreal’s multilingual migrants: Social identities and language attitudes after the proposition of the Quebec Charter of Values

2016· book-chapter· en· W2558214299 on OpenAlexaboutno aff
Ruth Kircher

Bibliographic record

VenueHope's Institutional Research Archive (Liverpool Hope University) · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLinguistic and Sociocultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCharterGovernment (linguistics)EthnocentrismImmigrationPolitical scienceIdentity (music)Ethnic groupGender studiesSociologyLawLinguistics
DOInot available

Abstract

fetched live from OpenAlex

Previous research by the author showed that at the beginning of the 21st century, multilingual migrants in Quebec’s urban centre Montreal shared the same attitudinal trends as the city’s non-migrants – most likely as a result of their shared, civic identity. However, these findings originate from a time at which the provincial government was strongly propagating a civic (rather than an ethnic) national identity. Then, in 2013, the provincial government proposed the Quebec Charter of Values, a bill putting forward the prohibition of religious symbols in the public sector. The Charter caused much controversy and was seen by many migrants as an act of ethnocentrism. In this chapter, Ruth Kircher presents the findings of a new, questionnaire-based study that investigates whether the Charter has caused changes in first- and second-generation immigrants’ social identities and in their attitudes towards French, the province’s official language, compared to English, the primary language of the rest of Canada and North America at large.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.034
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.030
GPT teacher head0.303
Teacher spread0.273 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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