Bibliographic record
Abstract
The utilization of Indigenous Knowledge in natural resource management frameworks, including environmental assessments, is increasingly being recognized as an important asset by the environmental community. This is because Indigenous Knowledge, which has been developed over generations by Indigenous communities, can lead to the development of more effective and equitable resource management practices that are aligned with local needs, cultural values, and ecological knowledge. At the same time, Participatory Geographic Information Systems (PGIS) are also emerging as a valuable tool for storing and mobilizing information towards natural resource management. Considering shifts in the environmental community around natural resource management and the use of IK, and the potential of PGIS, the incorporation of Indigenous Knowledge into PGIS raises important ethical questions regarding knowledge ownership and sharing, and there are challenges and limitations to implementing Indigenous Knowledge-based PGIS platforms. This research paper seeks to explore the application of Indigenous Knowledge in natural resource management through a scoping review or relevant literature, including case studies of PGIS platforms used by Indigenous communities, both in Canada and abroad. The paper addresses the following key questions: How can Indigenous Knowledge be applied towards natural resource management frameworks and environmental assessments? How can Indigenous knowledge be stored and mobilised within PGIS platforms for future generations? What are the challenges and limitations surrounding the implementation of Indigenous Knowledge within PGIS platforms in natural resource management? What are the ethics surrounding Indigenous Knowledge-based PGIS platforms? The paper reveals that integrating Indigenous Knowledge into natural resource management frameworks can lead to sustainable management practices. The paper also demonstrates that PGIS platforms have the potential to facilitate knowledge sharing and collaboration among communities, but their implementation requires careful considerations of the challenges and limitations associated within Indigenous Knowledge-based systems. Balancing the need to share knowledge across communities with protecting intellectual property rights and the cultural significance of Indigenous Knowledge is crucial. The implementation of PGIS platforms also requires significant technological infrastructure and skills, requiring significant human resources, which can be challenging for Indigenous communities with limited resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".