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Record W2013187100 · doi:10.1353/ces.2015.0010

Language Skills, Profiles, and Prospects among International Newcomers to Edmonton, Alberta

2015· article· en· W2013187100 on OpenAlexvenueaboutno aff
Albert Maganaka, Heather Mae Plaizier

Bibliographic record

VenueCanadian ethnic studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationReferralTest (biology)English languageRefugeePopulationMedical educationLanguage proficiencyLanguage barrierPolitical sciencePublic relationsPsychologyMedicinePedagogyFamily medicineMathematics educationEnvironmental health

Abstract

fetched live from OpenAlex

This article aims to build both community and scholarly knowledge of skills, aspirations, needs, and characteristics of the international newcomer adult population in Edmonton. It highlights the Canadian Language Benchmarks (CLB) test scores, socio-demographic profiles, as well as goals and plans among adult immigrants and refugee clients of the Language Assessment, Referral, and Counselling Centre (LARCC) in Edmonton, Alberta. LARCC includes both provincially and federally funded programs. It provides immigrants and refugees with a recognized assessment of their current level of English language proficiency; knowledge of local options and resources for relevant English language and occupational training; and helps newcomers explore their educational and career goals/opportunities. This article is largely practical rather than theoretical, presenting a practitioners’ perspective into how to better enhance the benefits of immigration for immigrants, in particular, and for the larger society as a whole. As such, following an empirical discussion, we conclude with recommendations based on concrete knowledge of language skill development challenges at different competency levels, and the labour market advantages of language proficiency. Likewise, we suggest concrete ways to better address the language, occupational skills, and integration opportunities afforded by having such a diverse new population arriving in Alberta. Cet article a pour l’objectif d’augmenter les connaissances dans les milieux communautaires et académiques au sujet des habilités, des espoirs, des besoins, et des caractéristiques de la population de nouveaux arrivants adultes à Edmonton. Il met en évidence les résultats des tests d’anglais, selon les Niveaux de Compétences Linguistiques Canadiens (NCLC), les caractéristiques sociodémographiques, ainsi que les objectifs personnels parmi les immigrants adultes et réfugiés, qui sont les clients du Centre d’évaluation linguistique, d'orientation, et de conseil (LARCC) à Edmonton. LARCC comprend deux programmes, financés par la province d’une part et par le gouvernement fédéral de l’autre part. Ces programmes fournissent aux clients une évaluation reconnue de leur niveau actuel de maîtrise de l’anglais. Les clients apprennent aussi, au besoin, les possibilités et les ressources locales pertinentes à une formation professionnelle, ou reliée à leur métier cible. Ils peuvent bénéficier de l’aide pour mieux explorer leurs buts éducatifs et professionnels. Cet article se veut pratique plutôt que théorique. Il présente le point de vue des agents qualifiés dans le secteur sur la façon de mieux valoriser les avantages de l’immigration pour les immigrants, en particulier, et pour la société dans son ensemble. En tant que tel, nous présentons une discussion empirique, suivie de recommandations fondées sur une connaissance concrète des défis de l’acquisition de langue à différents niveaux de compétences, et les grands avantages au marché du travail d’une bonne maîtrise de la langue. De même, nous proposons des moyens concrèts pour mieux apprécier les possibilités d’échange et d’intégration conférées par l’arrivée de tant de diverses langues, habiletés, et perspectives au sol albertain.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.150
GPT teacher head0.491
Teacher spread0.341 · 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 teacher head, 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

Citations1
Published2015
Admission routes2
Has abstractyes

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