An Appraisal of Institutional Framework for Guaranteeing Mine-Host Communities Right to Food in the ICGLR Sub-Region: Part II
Bibliographic record
Abstract
Absence of strong institutions worth to realize good governance in the mineral sector and protection of human rights seem a challenge that face not only natural resources-rich developing states but also sub-regional initiatives. Strong institutions are said to depict features such as; independency, relevant infrastructure, relevant expertise and command of political support from the state(s) and or other organs. The International Conference on the Great Lakes Region (ICGLR) sub-region has in place numerous regulatory organs with mandate over good governance in mineral sector and human rights preservation. Unlike other sub-regions in Africa, ICGLR is naturally endowed with plenty natural resources with regional and global value against the global threat of climate change. This paper, through qualitative review of primary and secondary documents namely; protocols, conventions, declarations, pacts, journal articles, books and internet sources examines how strong are the ICGLR institutions towards guaranteeing MHCs right to food. The study found that, despite of the established institutions at the ICGLR sub-regional level, they fall short of the international standards of robust institutions. Such institutions suffer from both legal and practical challenges namely; inadequate independency, inadequate human resources, little or no political will and feeble infrastructure. If the ICGLR sub-region intends to guarantee MHCs right to food, it may not escape eradicating the noted legal and practical challenges of established 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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".