As identidades do professor de inglês no ensino médio integral em escolas públicas do estado de Sergipe em tempos neoliberais
Bibliographic record
Abstract
This research was developed from 2019 to 2022 and sought to investigate which identities are expected, built and negotiated by the English teacher inserted in public full-time High School education (EMI) in Sergipe in neoliberal times, considering the over-responsibility of the individual and the competitiveness as essential elements of contexts guided by neoliberalism (LAVAL, 2019) and recognizing the processes that favor the perpetration of the private sector over public education, in a decentralization that makes room for its privatization at different levels and forms. In this way, theoretical contributions were needed that went beyond Applied Linguistics (ZACCHI, 2016) – the field in which I am inserted – to sociopolitically support historical issues raised during the text, such as full-time education and neoliberalism. Through a qualitative research, which uses the EMI implementation and maintenance documents in Sergipe (SERGIPE, 2016a; SERGIPE, 2016b; SERGIPE, 2016c; SERGIPE, 2016d; SERGIPE, 2016e; SERGIPE, 2021), a questionnaire and semi-structured interviews with teachers of English in practice in the pedagogical model in question gathered in a focus group for narrative analysis and data triangulation. So, I sought to recognize characteristics of the neoliberal agenda in legislation and in my act of narrating myself and the act of the participants narrating themselves with the aim of bringing new modes of subjectivation to the fore by the conditions of possibility (FOUCAULT, 2013a) not only of the modus operandi, but also of resistance in a Minor Education (GALLO, 2002) that opens space for exchange between teachers and researchers, justifying the relevance of this study.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".