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Critical ESL Education in Canada

2022· reference-entry· en· W4288759753 on OpenAlexaboutno aff
Sunny Man Chu Lau

Bibliographic record

VenueOxford Research Encyclopedia of Education · 2022
Typereference-entry
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyCritical pedagogyCritical theoryPedagogyCritical thinkingMaterialismIndigenousInterrogationCritical consciousnessEpistemologyPolitical science

Abstract

fetched live from OpenAlex

Critical approaches to English as a second language (ESL) education in Canada broadly fall under two intersecting orientations—inclusivity-focused and issue-focused. Inclusivity-focused education refers to critical approaches to ESL that valorize minoritized and/or Indigenous students’ voices, languages, and other semiotic resources in learning (in) English. This inclusive orientation aims to challenge systemic marginalization of multicultural voices and identities, destabilize static notions of languages and other modes of communication, and importantly, decolonize inequitable power structures inherent in academic and broader social setting. An issue-focused approach adopts an explicit critical agenda, using eco-social issues as the foci of curricular content to engage students in critical interrogation of social assumptions and participation in related class-based action research to simultaneously learn the language and enact change in broader communities. Recent trends in critical issue-focused inquiries also draw on posthumanist, socio-materialist, and Indigenous perspectives to offer more complex, interconnected, and distributed views of language learning and social change. These perspectives not only urge for alternative ways (cognitive, bodily, multi-sensory, affective, and spatial) of critical engagement but also a more human decentering perspective to understand the ethical interdependence of the human/non-human world.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.614
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0300.000

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 head0.046
GPT teacher head0.353
Teacher spread0.307 · 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
GenreOther

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

Citations2
Published2022
Admission routes1
Has abstractyes

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