Indigenous Ecological Knowledge and Perceptions of Climate Change on the Environment and Livelihood of Local Communities in Kgalagadi District of Botswana
Bibliographic record
Abstract
Extreme climate change causes an immeasurable threat to the livelihood security and prosperity of rural communities, including the natural environment and resources managed by the local people in the Kgalagadi District of Botswana. The study aims to understand the indigenous ecological knowledge of local communities about climate change, its impacts on the environment, and their livelihoods across five villages in the Kgalagadi District of Botswana. The present study used a semi-structured questionnaire survey at the household level who were randomly selected using the village register book. The results indicated that all the respondents in Kang village and some from Lehututu and Tshane villages perceived that the causes of climate change were unknown. However, some respondents across the other four villages believed that climate change is caused by various factors including wildfires, pollution from industries, impacts from livestock, and vehicles, as well as a curse from God. Indigenous knowledge must be well incorporated with scientific methods and up-to-date climate change adaptation and mitigation strategies to envisage more concrete results. This helps to integrate the insight of local people into policies and strategies to make an effort for solutions that are crucial for sustainable development. We suggest that all stakeholders should harmonise the use of indigenous knowledge with climate change strategies, to make the best use of its contribution to the successful execution of climate change policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".