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Record W4407865561 · doi:10.18806/tesl.v41i2/1408

Challenging the Tropes of Neoliberalism in Discussions of Newcomer Youth in Canada

2024· article· en· W4407865561 on OpenAlexafffundvenueabout
Sandra G. Kouritzin, Satoru Nakagawa, Taylor Ellis

Bibliographic record

VenueTESL Canada Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Manitoba
KeywordsNeoliberalism (international relations)SociologyPedagogyMedia studiesGender studiesSocial science

Abstract

fetched live from OpenAlex

In this re-examination of empirical data sets, we reconceptualize, from the perspectives of numerous stakeholders, the limits of identity positions available to immigrant adolescents who enter schools in immigrant-receiving schools and neighbourhoods already experiencing racism, intergenerational poverty, lack of social, economic, and cultural capital, and large numbers of English language learners. Research data referenced from three separate secondary schools in this context examined the social integration of English as a second language/additional language students within a variety of school-related contexts, related policies and students’ encounters with them, and students’ experiences of academic segregation and mainstreaming. Our re-reading of data through a lens critical of neoliberalism reveals how educational stakeholders, including students, buy into and replicate specific tropes of neoliberalism, which may be damaging in the long run. We urge educators to re-examine the language we use to describe students who are multilingual learners and that they consequently use to describe themselves.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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 designNot applicable
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
Published2024
Admission routes4
Has abstractyes

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