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Record W2942839121 · doi:10.1590/010318138654861490091

TRANSNATIONAL DIALOGUE ON LANGUAGE EDUCATION IN CANADA AND BRAZIL: HOW DO WE MOVE FORWARD IN THE FACE OF NEOCONSERVATIVE/NEOLIBERAL TIMES?

2019· article· en· W2942839121 on OpenAlexaffabout
Daniel de Mello Ferraz, Brian Morgan

Bibliographic record

VenueTrabalhos em Linguística Aplicada · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsYork University
Fundersnot available
KeywordsConversationNeoliberalism (international relations)SociologyPedagogyPoliticsFace (sociological concept)Relation (database)Critical pedagogyCultural studiesMedia studiesEpistemologySocial sciencePolitical scienceAnthropologyPhilosophyLaw

Abstract

fetched live from OpenAlex

ABSTRACT This interview with Prof. Dr. Brian Morgan from York University presents some of Dr. Morgan and Dr. Ferraz's perspectives in relation to language education in Canada and Brazil. The conversation plunges into essential topics to be problematized by language educators from both countries: neoconservative politics, neoliberalism, plurilingualism, philosophy of language (Derrida, Bakhtin, Foucault, Deleuze), cultural studies, teacher education, teaching practices. Brian Morgan invites us to go through a process of further thinking in terms of: 1. The Neoliberal agenda within educational policies and actions, 2. The relationship between theories (philosophies of language, cultural studies) and practices (how such theories impact - or not - public teachers' pedagogical practices), 3. The design of pedagogical projects (e.g., the Get Involved Project, MONTE MOR; MORGAN, 2014) that provide critical spaces for working within and against neoliberal agendas.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.598

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)0.0000.000

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 head0.350
Teacher spread0.333 · 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.

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

Citations3
Published2019
Admission routes2
Has abstractyes

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