Coming to Know and Knowing Differently: Implications of Educational Leadership
Bibliographic record
Abstract
Our paper will examine the question of counter-hegemonic knowledge production in the Western academy and the responsibilities of the Racialized scholar coming to know and producing knowing to challenge the particularity of Western science knowledge that masquerades as universal knowledge in academia. We engage the topic from a stance examining the coloniality of knowledge in educational leadership by centering Indigenous knowledge systems in the academy as a means to disrupt Euro-colonial hegemonic knowledging. We ask: How do we challenge the “grammar of coloniality” of Western knowledge and affirm the possibilities of a reimagining of “new geographies” and cartographies of knowledge as varied and intersecting ontologies and epistemologies that inform our human condition as “learning experiences, research, and knowledge generation” practices? The paper highlights epistemic possibilities of multicentricity, that is, multiple ways of knowledge as critical to understanding the complete history of ideas and events that have shaped and continue to shape human growth and development. The paper highlights Indigeneity as a salient entry point to producing counter-hegemonic knowing. The paper concludes pointing to implications for educational “re-search” and African educational futurity.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.068 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".