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Ritos de passagem: a memória, a identidade e o feminino em Ana Paula Tavares

2025· article· W7124460586 on OpenAlexaboutno aff
Kizze Nathianny Campos Viegas, Márcia Manir Miguel Feitosa

Bibliographic record

VenueLittera Revista de Estudos Linguísticos e Literários · 2025
Typearticle
Language
FieldPsychology
TopicMemory, Trauma, and Testimony
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)NarrativeCanadian literaturePostmodernism

Abstract

fetched live from OpenAlex

Propomos a discutir a importância da memória coletiva e sua relevância por meio de uma análise da obra Ritos de Passagem, da escritora angolana Ana Paula Tavares. Objetivamos entender como a literatura pode contribuir para a compreensão das mudanças sociais em tempos de crise e para a valorização de identidades diversas. Nesse contexto, a literatura e as concepções teóricas de autores como Inocência Mata (2007), Francisco Noa (2015), Pierre Bourdieu (2016) e Maurice Halbwachs (2006) desempenham papel fundamental. As obras literárias funcionam como espelhos sociais, levando-nos a questionar as estruturas opressivas e a encontrar o caminho para o entendimento de uma memória coletiva mais representativa e inclusiva. Ritos de Passagem oferece uma narrativa sobre a construção da memória coletiva, sua relação com a identidade individual e social e como a memória feminina é tão representativa e resistente na sociedade angolana. A narrativa transgeracional faz-se refletir sobre a conexão entre memória individual e coletiva, mostrando como as experiências passadas reverberam no presente. Concluímos que a memória, a identidade e o feminino são cruciais para o enfrentamento dos desafios e que a literatura desempenha um papel importante na compreensão das dinâmicas sociais e culturais emergentes em tempos de crise.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.008
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.017
GPT teacher head0.319
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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