Vetores de difusão do PLH: o espaço pedagógico e os FH
Bibliographic record
Abstract
There are many vectors in the dissemination of Brazilian Portuguese throughout the world.Their actions focus on behaviors and customs nourished daily by Brazilian cultures, among them, the most significant is language, as it brings together not only a set of linguistic signs, but also sociocultural practices and social cognition (Barsalou 1987), in an inseparable way (Saliés et al. 2022).In this article, we primarily focus on two vectors associated with Brazilian Portuguese as a Heritage Language (PHL): pedagogical practice in the teaching and learning of PHL and the Heritage Speakers (HS).For this purpose, we resorted to the literature on linguistic development and maintenance of minority languages in the light of language socialization (Duff 2016; He 2015) and the sociocognitive approach to language (Wittgenstein 2009 [1953]; Lakoff 1987).According to this literature, language maintenance is linked to literacy and the institutionalization of its teaching in spaces of socialization and crossing (Spitz et al. 2021) that allow HS to signify this "heritage" through and in the use of language as they construct and transform knowledge.Although lay knowledge considers the process an easy equation, theory shows us that it is multifaceted and complex.Among the questions we address are "Why isn't it enough to watch movies or listen to music to boost linguistic development?"and "How to act in the pedagogical space in favor of the development, maintenance, and dissemination of PHL?As we answer them, we outline a list of actions in the form of a pedagogical unit, capable of inspiring PHL teachers and leveraging linguistic development, language maintenance, and the participation of HS in the dissemination of Brazilian Portuguese.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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; both teacher heads agree on what is shown here.
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".