A Systematic Review of the Deployment of Indigenous Knowledge Systems towards Climate Change Adaptation in Developing World Contexts: Implications for Climate Change Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Countries in the developing world are increasingly vulnerable to climate change effects and have a lesser capacity to adapt. Consideration can be given to their indigenous knowledge systems for an integrated approach to education, one which is more holistic and applicable to their context. This paper presents a systematic review of the indigenous knowledge systems (IKSs) deployed for climate change adaptation in the developing world and advances implications for climate change education. A set of inclusion criteria was used to screen publications derived from two databases and grey literature searches, and a total of 39 articles constituted the final selection. Postcolonial theory’s lens was applied to the review of the selected publications to highlight indigenous people’s agency, despite IKSs’ marginalization through colonial encounters and the ensuing epistemic violence. The categories of social adaptation, structural adaptation, and institutional adaptation emerged from the IKS-based climate change adaptation strategies described in the articles, with social adaptation being the most recurrent. We discussed how these strategies can be employed to decolonise climate change education through critical, place-based, participatory, and holistic methodologies. The potential outcome of this is a more relatable and effective climate change education in a developing world context.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it