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Decolonizing Higher Education: Historical Myths, Official Discourses, and University Reforms in Brazil

2023· article· en· W4390056989 on OpenAlexvenueno aff
Naomar de Almeida Filho

Bibliographic record

VenueEncounters in Theory and History of Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSociology and Education in Brazil
Canadian institutionsnot available
Fundersnot available
KeywordsMythologyIdeologySubalternSociologyInstitutionPoliticsNarrativeIdentity (music)Power (physics)Gender studiesPolitical scienceHistorySocial scienceLawClassicsLiteratureAesthetics

Abstract

fetched live from OpenAlex

From a decolonial perspective, I propose in this paper a critical assessment of the university in Latin America as a social institution which, to fulfill its historical mission, needs to continuously recreate its institutional identity considering subaltern and peripheral economic, political and ideological contexts of coloniality. First, I provide a very brief account of the decolonial thought and its notions of ‘coloniality of power’, ‘coloniality of knowledge’ and ‘coloniality of being’. Secondly, in addition to revisiting historical grand narratives, I present a very brief history of higher education reforms in the Western world, followed by some highlights on the history of university reforms in Brazil. Then I introduce three index-cases of coloniality related to the Brazilian university to illustrate the topic. The first one I call the denial of Georges Cabanis, the second one has been called by historians as the Humboldt Myth, and the third one is a strong statement of my own responsibility: we Brazilians have never been Flexnerians. The specific discussion on how to interpret these emblematic index-cases of coloniality are my closing remarks for opening further debates on strategies and actions for decolonizing the University.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.560
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.318
Teacher spread0.299 · 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 teacher head, 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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