Decolonizing Higher Education: Historical Myths, Official Discourses, and University Reforms in Brazil
Bibliographic record
Abstract
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 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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".