Indigenous knowledge management: a catalyst for food security among Ghanaian yam farmers during COVID-19
Bibliographic record
Abstract
The research examined the influence of indigenous knowledge management on the food security of Ghanaian subsistence yam farmers amid the COVID-19 pandemic, utilising a descriptive correlation survey approach. The study encompassed 384 yam farmers selected using a multistage sampling procedure. Descriptive and inferential statistics were employed in the study. A statistically significant relationship was found between food security and various elements that support knowledge management processes. These factors, along with knowledge distribution (i.e. dissemination of indigenous knowledge) and knowledge conversion (i.e. adapting indigenous knowledge into community activities), jointly explained 74.0% of food security variation among peasant farmers during the pandemic. Indigenous knowledge management positively impacted food security in this context. Integrating indigenous knowledge with modern agricultural practices could enhance productivity and sustainability. The study underscores the importance of involving local communities in designing food security interventions for lasting positive impacts on livelihoods. It recommends the active participation of local people in shaping such interventions.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".