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Record W1775055485

Language Discrimination at English Dominant Workplaces

2015· article· en· W1775055485 on OpenAlexaffabout
Dulani Suraweera

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLinguisticsStress (linguistics)PsychologyStandard EnglishFirst languageGrammarContext (archaeology)Language transferLanguage proficiencyBritish EnglishWorld EnglishesSecond-language acquisitionRhetorical questionVocabularyComprehension approachLanguage educationHistory
DOInot available

Abstract

fetched live from OpenAlex

Immigrants who perceive themselves as proficient speakers of English may still experience challenges when they speak to native speakers of English in “inner circle countries”(Kachru,1978).  Blommmaert (2007) argues that language expertise are relatively  perceived and such  linguistic expertise in their home countries can be looked down upon in the new country as inappropriate in the context and genres of the specific work place(as cited in Roberts, 2010). In addition, Scassa(1994) states that dominant speakers make use of language standards as a gatekeeper to keep their positions and roles exclusive in a society while controlling access to standard language acquisition by non-standard speakers. Thus, this paper tries to investigate ways in which L2 speakers are discriminated in English-dominant workplaces in English speaking countries. The theoretical framework of this study stems from the view that language standards imposed by dominant speakers for enacting discrimination in multilingual and monolingual settings (Sik Hung Ng, 2007) relate to both code of the language (grammar, vocabulary) and the manner of speaking (Scassa, 1994).  A language code that can be subject to discrimination at the workplace is L2  speaker’s communicative and rhetorical styles. L2 anxiety and communication apprehension  can also have a very strong impact on L2 speaker’s manner of speaking (Shabani,2012; Rose, 2008; MacCroskey, 1977, p.78 as cited in Aly & Sterling Gowing, 2001). Nguyen (1993) posits that nonstandard accents, which is a manner of speaking, are discriminated in workplaces than the speaker’s appearance as accent diffuses social information about the speaker (Hansen, Rakic & Steffens, 2014). Another reason L 2 speakers are judged unfairly is due to their lack of pragmatic and strategic competencies (  Roberts, 2010). An extensive literature review was conducted in search of  ways in which L2 speakers’ language codes and the manner of speaking are subjected to workplace discrimination  in inner circle countries such as the UK, the US, Canada, and Australia. A descriptive approach is employed, with extensive review of related literature and the use of relevant secondary data. Findings reveal that factors such as language anxiety, communicative apprehension affect the language use of second language speakers which is perceived negatively by standard speakers(Shabani,2012; Rose, 2008; MacCroskey, 1977, p.78 as cited in Aly & Sterling Gowing, 2001 ). Furthermore, differences in accent and rhetorical styles as well as pragmatic and strategic incompetence  provide  grounds for workplace subjugation (Hansen, Rakic & Steffens , 2014; Roberts, 2010 ). As discussed in this paper, it is clear that L2 speakers experience workplace discrimination as their ability is undervalued based on their L2 interactions at the workplace. Thus, there are a number of concerns to be addressed. Most importantly, It is important to reconsider the availability and accessibility  of newcomer language programs in Canada to immigrants with all proficiency levels.  Furthermore, dominant English speakers need to be  more empathetic and effortful in negotiating meaning while differences in L2 interactions should not be seen as deficiencies that judge L2 speakers overall skills and personality.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.999

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)0.0020.000

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 head0.036
GPT teacher head0.237
Teacher spread0.200 · 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.

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
Published2015
Admission routes2
Has abstractyes

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