Academic scientific knowledge and Indigenous worldviews: Discourse integration for sustainable development
Bibliographic record
Abstract
How far has sustainable development that has dominated the agendas of politicians, academics, and world organizations for the past four decades gone? Do politicians, academics, and organizations have to look elsewhere for solutions? This article attempts to answer the second question: There may be solutions other than politics and the academy to promulgate sustainable development. The article argues that Indigenous knowledge is a fundamental domain in selecting the criteria for sustainable development and the design of corresponding goals for sustainability in the global economy. In this theoretical article, the intellectual traditions of the academy that emphasize academic detachment and objectivity are on the attack as representing thought that has invented Indigenous worldviews as the “other” because they provide a context with meaning and values considered unscholarly pursuits. The article examines how the academy can graft Indigenous worldviews and cultural ideas onto academic knowledge and technology in a way that considers Indigenous knowledge critical in creating knowledge for sustainable development. The discussion debunks Eurocentric objective traditions that solely set agendas for sustainability. This article calls for the academy to create a model that places Indigenous knowledge in a conspicuous place in the scholarly agendas of sustainable development. The article develops a collaborative model for the academy and indigenous worldviews. It concludes that Indigenous knowledge and academic traditions can collaboratively support policy actors such as world governments and politicians to find solutions for sustainable development.
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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 | 0.035 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.017 | 0.071 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".