TRANSNATIONAL DIALOGUE ON LANGUAGE EDUCATION IN CANADA AND BRAZIL: HOW DO WE MOVE FORWARD IN THE FACE OF NEOCONSERVATIVE/NEOLIBERAL TIMES?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.029 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".