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Record W2943828364 · doi:10.12957/pr.2019.40964

REIMAGINING NEOLIBERALISM, GLOBALIZATION, LITERATURE AND LANGUAGE EDUCATION: AN INTERVIEW WITH PROF. DR. DIANA BRYDON / Reimaginando o neoliberalismo, a globalização, a literatura e a educação linguística: uma entrevista com Prof. Dr. Diana Brydon

2019· article· pt· W2943828364 on OpenAlexaffabout
Diana Brydon, Daniel de Mello Ferraz

Bibliographic record

VenuePensares em Revista · 2019
Typearticle
Languagept
FieldArts and Humanities
TopicLinguistics and Education Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGlobalizationNeoliberalism (international relations)HumanitiesSociologyLibrary sciencePolitical sciencePhilosophySocial scienceLawComputer science

Abstract

fetched live from OpenAlex

In this interview, Dr. Brydon insightfully discusses contemporary keywords such as neoliberalism and globalization and how they intersect with literary studies and language education. She also talks about the transnational literacies projects developed between Canada and Brazil along the past ten years. Dr. Brydon also discusses the dichotomy between literature and language/linguistic studies by suggesting the reimagination of both fields. In the case of literature, she introduces speculative literature as a possibility to (re)imagine “horrific dystopian worlds” – on one hand, and a “world where people value negotiation, compromise, and solutions that may not be ideal to any of the partners but that are livable for all of us” – on the other.

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)0.014
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0190.018
Scholarly communication0.0100.010
Open science0.0010.008
Research integrity0.0050.018
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 head0.023
GPT teacher head0.308
Teacher spread0.285 · 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 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
Published2019
Admission routes2
Has abstractyes

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