‘Throwing a Baby with Bathwater,’ Restoration of the Tanzanian Indigenous Justice System: The Case of Sukuma, Kinga and Iraqwi Ethnic Groups
Bibliographic record
Abstract
Abstract The indigenous justice systems were modes of resolving conflicts in Tanzanian communities for millenia before the introduction of the common law system as it was applied in England. The introduced mode, despite its success, is encumbered with a number of challenges. Apart from the challenges, the restoration of one’s customs and traditions is what makes one a human. The conventional justice system being ‘water’ to clean off dirt, the ‘baby’ is celebrated for what it has so far achieved; thus, the washed baby should not have been thrown into the water because in Africa, and Tanzania in particular, no one denies how valuable a baby is to parent and the community at large. Despite Tanzania’s efforts in capturing the bits of indigenous justice systems, the laws in place to a great extent roll on the bits of the conventional justice system. Protecting and preserving one’s customs has caught the interest of the international and regional community. That should awaken Tanzanians to look for the baby (indigenous justice systems) and appreciate its beauty. Ratification of the convention on tribal and indigenous people is optional; its negation devalues one’s customs and traditions. This paper comes with a reformatory agenda. The pumpkin in the homestead cannot be uprooted i.e. indigenous traditions must be preserved.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".