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Record W2084787755 · doi:10.1002/tesq.110

Plurilingualism and Curriculum Design: Toward a Synergic Vision

2013· article· en· W2084787755 on OpenAlexaff
Enrica Piccardo

Bibliographic record

VenueTESOL Quarterly · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultilingualismTranslanguagingLinguisticsCurriculumSociologyCompetence (human resources)PedagogyApplied linguisticsPsychology

Abstract

fetched live from OpenAlex

Contemporary globalized society is characterized by mobility and change, two phenomena that have a direct impact on the broad linguistic landscape. Language proficiency is no longer seen as a monolithic phenomenon that occurs independently of the linguistic repertoires and trajectories of learners and teachers, but rather shaped by uneven and ever‐changing competences, both linguistic and cultural. In the European context, research conducted over the past 20 years in multilingual realities of local communities and societies has brought to the forefront the notion ofplurilingualism, which is opening up new perspectives in language education. In North American academia, the paradigm shift from linguistic homogeneity and purism to heteroglossic and plurilingual competence in applied linguistics has been observed in the emergence of such concepts asdisinventing languages,translanguaging, andcode‐meshing. Starting from a historical perspective, this article examines the shared principles upon which such innovative understandings of linguistic competence are based. In particular, it investigates the specificity of plurilingualism as an individual characteristic clearly distinct from multilingualism in the light of different theoretical lenses. The author discusses the potential of such vision together with its implications. Finally, this article offers pedagogical implications for English language education in the North American context, and suggests ways to investigate the new active role that English language learners and teachers can adopt in shaping their process of learning English.

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)0.009
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0030.018
Scholarly communication0.0110.012
Open science0.0020.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.230
Teacher spread0.213 · 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 designTheoretical or conceptual
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

Citations225
Published2013
Admission routes1
Has abstractyes

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Same venueTESOL QuarterlySame topicSecond Language Learning and TeachingFrench-language works237,207