Decolonization of Postgraduate Education Through Indigenous Two-Eyed Seeing Methodology
Bibliographic record
Abstract
This chapter outlines the fundamental principles and practices of the two-eyed seeing methodology and its potential to reshape postgraduate education. It explores the implications of decolonization, emphasizing the need to challenge existing Eurocentric frameworks and prioritize Indigenous perspectives, knowledge, and methodologies. The chapter explores the potential for incorporating Indigenous ways of knowing, ethical research practices, land-based learning, and integrating Indigenous languages and worldviews. Through these efforts, postgraduate programs can contribute to revitalizing Indigenous cultures, support self-determination, and address the complex challenges of our time. By centring Indigenous perspectives and practices, postgraduate programs can contribute to the reconciliation process, promote social justice, and cultivate a generation of scholars equipped to address the urgent challenges of the future through a lens of cultural diversity and intercultural understanding.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".