Bibliographic record
Abstract
Developed through the Decolonization and the Study of Religion Workshop Series, this paper will explore some concerns about decolonizing pedagogy in theory, practice, and the classroom. Weaving insights from a set of important thinkers in the field – like Achille Mbembe, Linda Tuhiwai Smith, Eve Tuck and Ruben A. Gaztambide-Fernandez, Walter Mignolo and Catherine Walsh, Anabal Quijano, and Paulette Regan – the aim of the paper is to introduce some questions for pedagogues to think about in relation to the question of decolonizing pedagogies and to some of the discussions had at the workshop. The paper explores topics and discussions about structural critiques of the university, material versus epistemic analyses of decolonization, learning and unlearning as a central method in decolonization, the importance of how to make space for African and Indigenous Traditional Knowledges, and thinking about how to unpack power relations in the classroom and curriculum. The paper is more concerned with opening dialogue and making space for insights than an attempt to answer definitively questions of decolonization and pedagogy.
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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.010 |
| 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".