Understanding the manifestation of the conflict between traditional leaders and ward committees, A case of Greater Giyani Municipality, Limpopo Province, South Africa
Bibliographic record
Abstract
In South African municipalities, there has been a coexistence between elected and traditional leaders for decades. However, this coexistence has often been marked by numerous conflicts. These conflicts take different forms and dimensions; while priority has been given to violent conflicts, these conflicts escalate and rescale when not dealt with. However, it appears priority has been given to violent conflicts, making it critical to understand and deal with all forms of conflicts before they escalate. Therefore, this study examined the nature of the conflict in the Greater Giyani municipality of South Africa. Purposive sampling was used to select 33 participants. Interviews were conducted with knowledgeable individuals to gain insight into the tensions between traditional and elected leaders. Codes and themes were electronically generated from the narratives of community members and key informants using ATLAS ti.22. The study concluded that existing conflict resolution mechanisms focus more on violent conflicts than embedded ones. Again, the current study demonstrated that non-violent conflict, if not dealt with, can escalate into violent conflict. Thus, these research findings pointed to the need for state and non-state actors to pay attention to all forms of conflict. By identifying different types of conflicts between community leaders, this study contributes to understanding how conflicts between TLs and WCs manifest and highlights the extent of common conflicts. Keywords: active conflicts, overt, nature of conflicts, peace institutions
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".