The feminization of PLH (Portuguese Language Heritage): stories of agency, belonging, and the maintenance of Portuguese as a heritage language
Bibliographic record
Abstract
O presente trabalho investigou os processos de feminização do PLH e suas interseccionalidades, no intuito de entender como essas brasileiras se (re)organizam para (re)existir politicamente na nova sociedade de acolhimento por meio da Língua Portuguesa e de contribuir para a construção de conhecimento na área. Para tanto, a pesquisa de natureza qualitativa, interpretativista (DENZIN; LINCOLN, 2006) de inspiração auto etnográfica (YAZAM; CANAGARAJAH; JAIN, 2020), analisou as narrativas de seis brasileiras residentes na área da Grande Vancouver, província da British Columbia. Fundamentada na perspectiva da linguística aplicada (DUFF, 2015), indisciplinar (MOITA-LOPES, 1998; 2006) e crítica (PENNYCOOK, 1998; 2006), o presente estudo valeu-se de diferentes instrumentos de geração de dados, a saber, observação participante em comunidades virtuais, diário de campo, questionário sociocultural, entrevistas em profundidade e grupos focais. Mobiliza as noções de agência (AHEARN,2001; DURANTI, 2004, MAHMOOD SABA, 2019), pertencimento (DUSZACK,2005; LINDE, 1993; ALSOP,2005; SNOW,2001, HOLMES; MARRA, 2004), accounts (ARUNDALE,1999; SCHIFRIN,1994), assim como estudos nas áreas de feminização da imigração (RODRIGUEZ, VILLANÓN, CASTRO, 2019, JULIANO,2012, GIL,2013;GREGORIO,2004), interseccionalidade(CRENSHAW, 2002; SILTANEN; DOUCET, 2017) e analise da narrativa (LABOV,1997, LINDE,1993; DE FINNA,2000, BAMBERG, 2002, MISHLER, 2002; BIAR, 2012, BASTOS E BIAR, 2015. As análises indicaram que é por meio do português, seja na manutenção, no ensino ou como língua de mediação das interações nas comunidades virtuais, que essas mulheres criam sentidos de pertencimento e justificam suas ações (accounts)
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".