Building a Meaningful Bridge Between Indigenous and Western Worldviews: Through Decolonial Conversation
Bibliographic record
Abstract
In this paper, Indigenous and non-Indigenous scholars used a decolonial conversation framework to build a meaningful bridge between Indigenous and Western worldviews. Our decolonial conversations approach is a unique and transformative space where Indigenous and Western knowledge systems intersect, facilitating a rich exchange of valuable insights for fostering intercultural dialogue and breathing new ways of knowing and acting into Indigenous cultures. The decolonial conversation provides a platform for transmitting Indigenous knowledge and cultural practices across generations by uniting Indigenous land-based knowledge, community members, and Western researchers. Integrating Indigenous and Western knowledge systems in these environments fosters collaboration, dispels stereotypes, and forges partnerships grounded in reciprocity and trust. Through this collaborative process, traditional cultural camps emerge as potent catalysts for instilling cultural pride, fostering community resilience, and co-creating knowledge. This collaborative approach aligns with the broader objectives of decolonization and cultural revitalization. In our exploration following the decolonial learning conversation, we, comprising an Indigenous woman land-based educator and a racialized academic scholar, focused on the transformative potential and synergies realized by integrating these knowledge systems within the context of traditional cultural camps.
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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 | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.030 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".