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Record W6992283239

“La langue est la porte d’entrée pour un sentiment d’appartenance”: An Investigation of the Complex Relationship Between Language and Sense of Belonging among Second-generation Arab Canadian Young Adults in Montreal

2021· dissertation· W6992283239 on OpenAlexfundaboutno aff

Bibliographic record

VenueTSpace · 2021
Typedissertation
Language
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsCultural assimilationAgency (philosophy)Relation (database)Sense of placeSense of agencyNeuroscience of multilingualismArabicSense of community
DOInot available

Abstract

fetched live from OpenAlex

Immigration, an important component of the population growth of Canada, has resulted in an increasing diversity that has changed the demographic, religious, and linguistic make-up of the country. Two topics, sense of belonging and language, are frequently brought into question in relation to immigrants, and specifically the second-generation. Despite the strong connection between the two categories, Canadian studies rarely explored the relationship between them. To address this gap, this research investigates the relationship between language and sense of belonging among second-generation Arab Canadian young adults in Montreal, Canada, using an intersectional lens. Following case study methodology, I conducted two 90-minute semi-structured interviews with 14 second-generation Arab Canadian youth, ages 20-33, to examine their identities, territorial sense of belonging to Canada, the province of Quebec, the city of Montreal, and to their heritage countries, and factors that may impact their sense of belonging. I also examined their language practices and language attitudes towards French, English and Arabic. Findings showed that participants’ strongest sense of belonging was to the city of Montreal, followed by Canada/French-speaking Canada. Most participants revealed the complexity of having multiple identities and belonging. Findings also identified participants’ upbringing, educational and social experiences as three main factors that may have impacted their sense of belonging. Participants recalled instances of discrimination in schools, in the labour market, and on the streets of Montreal, which they resisted with agency or assimilationist strategies. For most participants, French/English bilingualism was crucial for studying and for obtaining employment, while Arabic had an affective role for some, in relation to family and religion. Standard varieties of French were favoured among many participants due to their formality, yet a Quebec French accent was perceived as an asset for employment. It was also found that the Quebec accent was a site of power, causing inequalities. As for the relationship between language and sense of belonging, it was found that a positive attitude towards a language/accent corresponded to a sense of belonging to the territory related to that language/accent, and vice-versa. However, neither language proficiency nor the frequency of language/accent usage necessarily corresponded with territorial sense of belonging. L'immigration, composante importante de la croissance démographique du Canada, a entraîné une diversité croissante qui a modifié la composition démographique, religieuse et linguistique du pays. Deux sujets, le sentiment d'appartenance et la langue, sont fréquemment évoqués en ce qui concerne les immigrants, et plus particulièrement la deuxième génération. Malgré le lien étroit entre ces deux éléments, peu d’études canadiennes ont exploré leur relation. Pour combler cette lacune, cette recherche examine la relation entre la langue et le sentiment d'appartenance chez les jeunes adultes canadiens arabes de deuxième génération à Montréal, Canada, en utilisant une lentille intersectionnelle. En suivant la méthodologie de l'étude de cas, j'ai mené deux entretiens semi-structurés de 90 minutes avec 14 jeunes canadiens arabes de deuxième génération, âgés de 20 à 33 ans, afin d'examiner leurs identités, leur sentiment d'appartenance territoriale au Canada, à la province du Québec, à la ville de Montréal et à leurs pays d'origine, ainsi que les facteurs qui peuvent avoir un impact sur leur sentiment d'appartenance. J'ai également examiné leurs attitudes envers le français, l'anglais et l'arabe. Les résultats montrent que le sentiment d'appartenance le plus fort des participants est celui envers la ville de Montréal, suivi du Canada, ou du Canada francophone. Ceux-ci ont également identifié l'éducation, les expériences éducatives et sociales des participants comme étant les facteurs principaux qui ont pu affecter leur sentiment d'appartenance. Les participants se souviennent aussi de cas de discrimination auxquels ils ont résisté par des stratégies d'agence ou d'assimilation. Pour la plupart des participants, le bilinguisme français/anglais est crucial pour l’étude et l’emploi, tandis que l'arabe a un rôle affectif pour certains. Les variétés standard du français sont privilégiées par de nombreux participants tandis que l'accent québécois est perçu comme un atout pour l'emploi, mais aussi comme un lieu de pouvoir, causant des inégalités. Quant à la relation entre la langue et le sentiment d'appartenance, les données montrent qu'une attitude positive envers une langue/accent correspond à un sentiment d'appartenance au territoire lié à cette langue/accent, et vice-versa. Cependant, ni la maîtrise, ni la fréquence d'utilisation de la langue/accent ne correspond nécessairement au sentiment d'appartenance territoriale.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.033
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0120.005
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.377
Teacher spread0.336 · 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
Published2021
Admission routes2
Has abstractyes

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