Decolonizing Journalism Education: Integrating Global Indigenous Knowledge Systems and Upholding Educational Sovereignty
Bibliographic record
Abstract
This essay explores the concept of decolonizing journalism education through the integration of Indigenous knowledge systems, focusing on educational sovereignty. Drawing from the story of my grandmother—an African Indigenous woman skilled in pottery, traditional medicine, and storytelling—it highlights how Indigenous knowledge offers a rich, immersive learning experience outside formal schooling. These practices, rooted in cultural heritage and holistic understanding, challenge the rigid structures of Western education. By integrating Michelle Bishop’s framework of Indigenous education sovereignty, which includes elements such as intergenerational reciprocity, agency, time, pattern thinking, country, and relationality, this essay advocates for an innovative and transformative approach to journalism education. The essay also uses the work of Indigenous scholars from Canada, Africa, Australia, and New Zealand to provide a global perspective on educational sovereignty. It argues for moving beyond simply adding Indigenous content to reimagining education that centres Indigenous ways of knowing. Through this framework, journalism programs can become more inclusive, fostering a dynamic learning environment that values deep cultural understanding and self-determination.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".