Teaching Portuguese as an Additional Language and Academic Writing: The Co-Construction of Multilingual Identities in Telecollaboration Activities between Brazil and Canada
Bibliographic record
Abstract
Teaching Portuguese as an Additional Language (PAL) in multicultural contexts presents both a challenge of learning a new language and an opportunity to employ methodologies for teaching academic writing. In this chapter, we discuss the methodological principles underlying the telecollaboration activities and learning paths used in our approach. These activities facilitated intercultural interactions and experiences with various academic genres among the participating student groups. The telecollaboration between Brazil and Canada promoted multilingualism, which was instrumental in fostering both linguistic and cultural development. Digital tools played a crucial role in enhancing academic writing skills, contributing to the formation of intercultural identities. One of the primary challenges in teaching PAL was helping students navigate the complexities of academic writing in a non-native language. This required not only developing linguistic proficiency but also gaining a deep understanding of academic discourse, which is often shaped by cultural specificities. The use of appropriate educational resources and materials was essential in this process, as was the incorporation of digital tools and technologies that supported the development of academic writing skills in PAL. The chapter concludes with reflections on issues that recent research on academic writing has not extensively explored, particularly methodologies that integrate telecollaboration, identity, intercultural issues, and the production of various written genres. These reflections highlight the challenges faced by PAL instructors in teaching academic writing within the theoretical and methodological frameworks presented in this chapter, aiming to promote a more comprehensive view of education in global contexts
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".