Indigenous Knowledge Systems for Local Weather Predictions: A Case of Mukonchi Chiefdom in Zambia
Bibliographic record
Abstract
The purpose of the study was to unravel constituents of the indigenous knowledge systems (IKS) and appreciate people’s experiences in predicting the weather in daily undertakings. The objectives of the study were; to identify factors or systems used, establish the knowledge used in predicting the weather and compare the indigenous and current scientific method of predicting the weather. Qualitative and quantitative research designs were used. Primary data was collected through semi structured, face-to-face and in-depth interviews. This was complemented by secondary data collected through desk reviews of relevant published materials. The findings reveal that indigenous knowledge systems have been employed by people of Mukonchi chiefdom since time immemorial. There has also been reliance on IKS to make decisions pertaining to livelihoods such as agricultural activities. However, IKS in the area remains undocumented. Observation of several occurrences in combination or singularly relating to plants, animals, insects and astronomical events were factors of significant importance in the knowledge of weather extrapolation. Elements such as age, frequency of use of the IKS and level of education were seen to be of momentous prominence in utilisation of the indigenous knowledge as modern means of weather forecasting which are applicable to local community environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".