Beyond the Extractive Paradigm: Appraising Indigenous Knowledge in South African Environmental Governance
Bibliographic record
Abstract
This study provides a critical examination of the integration of Indigenous Knowledge Systems (IKS) into environmental governance (EG) in South Africa, emphasising both the potential benefits and the challenges posed by the existing extractive paradigm. This research uses postcolonial Indigenous theory and a qualitative narrative methodology to analyse the implications of relegating Indigenous people to peripheral roles within stakeholder frameworks. It critiques the reductionist approach that treats IKS as supplementary to contemporary governance structures, which often undermines the comprehensive contributions of Indigenous values, spirituality, and leadership to sustainability initiatives. Drawing comparative insights from Canada, this study contextualises the specific difficulties faced by South Africa in effectively incorporating IKS into EG frameworks. The key findings suggest that epistemic neo-colonialism and a narrowly utility-driven emphasis obstruct the meaningful integration of IKS, thereby marginalising its cultural and spiritual dimensions. Consequently, the study advocates for a holistic approach that prioritises Indigenous self-determination and recognises the intrinsic value of their knowledge systems. The conclusions emphasise the necessity for transformative policies that transcend tokenistic inclusion and enable IKS to authentically inform sustainable practices. This research contributes significantly by reframing IKS as a substantial framework for achieving ecological balance and intergenerational equity. It highlights practical implications for policy reform, particularly the need for governance models grounded in Indigenous philosophies such as Ubuntu. Additionally, the study addresses social implications, including promoting cognitive justice and equitable participation in environmental decision-making processes. The limitations of this research include the narrow scope of case studies and the requirement for empirical data to supplement the theoretical analysis. Overall, this study enriches the discourse on decolonising environmental governance and underscores the importance of confronting structural barriers to the ethical and effective integration of IKS in South Africa and beyond.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".