Teaching English as a Lingua Franca in Brazil: Insights into Materials Writing
Bibliographic record
Abstract
In this paper, I intend to examine the main English as a lingua franca (ELF) issues discussed in the National Common Curricular Base (Brasil, 2018) and compare them to the views put forward by mainstream scholars in the field (Baker, 2016, 2018; Dewey, 2007; Jenkins, 2006, 2012, 2015; Pennycook, 2006, 2009; Widdowson, 1994). In addition, as a researcher and creator of teaching materials, I intend to share some insights into materials writing by presenting the main strategies adopted in writing a series of English textbooks for pre-teens, evaluated and approved for distribution by the Brazilian Textbook Program, that I have written with Tavares (Franco & Tavares, 2018). Therefore, I hope this article may help shed light on developing and implementing materials that adopt an ELF-oriented approach in a scenario created by the legislation and the selection of textbooks that seems to be promising for the establishment of the ELF paradigm in Brazil.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".