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Record W2599193626 · doi:10.18806/tesl.v33i0.1250

Empowerment of Refugees by Language: Can ESL Learners Affect the Target Culture?

2017· article· en· W2599193626 on OpenAlexvenueno aff
Fereshteh Tadayon, Ali Khodi

Bibliographic record

VenueTESL Canada Journal · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationMainstreamSociologyAcculturationAffect (linguistics)Language acquisitionEmpowermentRefugeeMulticulturalismVariety (cybernetics)Lingua francaIdentity (music)Language educationLinguisticsPolitical sciencePedagogyAesthetics

Abstract

fetched live from OpenAlex

Numerous studies have investigated the changing patterns of immigration, the growth of multicultural-multilingual societies, and the important role of language in identity construction. Unfortunately, the issue of identity construction is affected by a variety of factors such as language learning and acquisition underlying different contexts and cultures, whereas, to some extent, the effects of language attrition and acculturation on the target community have not received the necessary attention. This crisis is defined primarily as the changes that occur in societies as the result of the amalgamation of languages for communication. It has been clearly stated by Kramsch (2008) that language has the potential to affect speakers’ minds and identities. Moreover, immigrants’ liberatory autonomy can empower them to be critical thinkers in new societies (Allwright & Hanks, 2009). This raises the question of how to preserve the distinguishing features of societies from potential cultural and social changes brought about by the people who use languages other than the national language of a country. Further elaboration on the effects of language in empowering immigrants is highly recommended. In this article, given the importance of patterns of immigration underlying second language acquisition and English mainstream education, we discuss the diminishing cultural and linguistic traces of non-English-speaking immigrants as a result of the encouraged loss of their first languages. De nombreuses études se sont penchées sur les tendances changeantes de l’immigration, la croissance des sociétés plurilingues et le rôle important de la langue dans la construction identitaire. Malheureusement, alors que la question de la construction identitaire est affectée par divers facteurs tels l’apprentissage et l’acquisition d’une langue dans différents contextes et différentes cultures, les effets de l’érosion des langues et l’acculturation de la communauté cible n’ont pas reçu toute l’attention voulue. Cette crise se définit principalement par les changements qui ont lieu dans les sociétés en raison de l’amalgamation des langues pour la communication. Kramsch (2008) a affirmé clairement que la langue a le potentiel d’affecter l’esprit et l’identité des locuteurs. De plus, l’autonomie libératoire des immigrants peut les habiliter à penser de manière critique dans les nouvelles sociétés (Hanks & Allwright, 2009). Cela soulève la question à savoir comment préserver les traits distinctifs des sociétés des changements culturels et sociales qui pourraient découler des actions de personnes qui parlent des langues autres que la langue nationale d’un pays. Nous recommandons fortement de poursuivre l’étude des effets du langage sur la responsabilisation des immigrants. Compte tenu de l’importance des tendances de l’immigration sous-jacentes à l’acquisition d’une langue seconde et à l’éducation régulière en anglais, nous discutons de la diminution des traces culturelles et linguistiques des immigrants allophones découlant du manque d’encouragement à préserver leur première langue.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0080.005
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.009
GPT teacher head0.244
Teacher spread0.235 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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