A Systematic Review of the Deployment of Indigenous Knowledge Systems towards Climate Change Adaptation in Developing World Contexts: Implications for Climate Change Education
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".