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Record W7039143043

Lenda e tradição oral em Legends of Vancouver, de Emily Pauline Johnson

2018· dissertation· pt· W7039143043 on OpenAlexaboutno aff

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2018
Typedissertation
Languagept
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeOral historyPower (physics)Subject (documents)Mythology
DOInot available

Abstract

fetched live from OpenAlex

A tradição oral pode ser considerada como a base da transmissão do conhecimento de uma geração para a outra dentro das comunidades indígenas. Foi através das narrativas orais que os povos nativos mantiveram seus laços coesos e suas estórias em constante movimento. O objetivo da oratória indígena era transmitir as lendas, os mitos e as canções das comunidades, fazendo com que os aspectos culturais e identitários dos grupos fossem compartilhados e preservados ao longo do tempo. Contudo, as narrativas orais não eram moldadas para serem lidas, mas antes compartilhadas por meio de performances de contação de estórias. Ou seja, a literatura das sociedades ameríndias era uma literatura de caráter oral, idealizada e difundida pelos mecanismos da tradição oral. Assim sendo, a presente dissertação tem por objetivo analisar a obra Legends of Vancouver (1911), de Emily Pauline Johnson, levando em consideração o modo como a autora por meio do texto literário em um formato escrito resgatou o gênero folclórico lenda e a tradição oral para construir lendas literárias que espelham a performance oral. Pauline Johnson permeou suas narrativas lendárias com as marcas da oralidade, ao recriar dentro do texto literário escrito a ambientação da contação de estórias mediante a presença de um contador e um ouvinte que por muitas vezes é também um interlocutor.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.289
Teacher spread0.259 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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