The importance of a political approach in english teaching in times of neoliberalism
Bibliographic record
Abstract
This current piece of work reflects upon the relationship between neoliberal theoretical principles and English teaching In Brazil, showing how it is influenced by them, taking the Base Nacional Comum Curricular (BNCC) (BRASIL, 2018) into account, the document that works as a compass for Brazilian education. Particularly, the reflection starts with the definition of the concept of neoliberalism, by Safatle, Junior and Dunker (2021) and Dardot and Laval (2014), to analyze if, and in what way, it could be exerting influence in the BNCC (BRASIL, 2018). In this endeavor, it is noted proximity between neoliberal ideas and the principles taken as a horizon for the curriculum and for the development of the English teaching-learning process. It is also observed that there are adjacent themes to this that need to be addressed, such as the definition of capitalism, imperialism and linguistic imperialism (LENIN, 1999; PHILLIPSON, 1992;). Thus, it is evident that the English teaching-learning process and other curricular components are not void of political matters and, so, they must be taken critically, an aspect that also justifies this research. When it comes to the methodology, this research focuses on the BNCC (BRASIL, 2018), through descriptive-analytic and bibliographic lenses, and in neoliberal concepts that are especially related to English teaching. From that, it is concluded that the teacher who is responsible for the subject of English acts on political grounds, and reflections about the possible courses of actions and the importance of a political approach in teaching are made.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".