Human Security Assessment in Kenya—The Case of Isiolo, Lakipia, Nandi and Elgeyo Marakwet Counties (Note 1)
Bibliographic record
Abstract
While human security concerns are critical in sub-Saharan Africa, initiatives to incorporate a human security orientation in development policies and planning are still rare. A human security approach to development requires the identification and integration of the needs of the vulnerable groups into development strategies of a country. A human security assessment is an effective tool for identifying threats to sustainable development and the factors that cause vulnerability of communities due to their geographic, demographic, and socio-cultural differences, and variations in the nature of institutions for development in their communities. This study presents the findings of a human security assessment conducted in four counties (Isiolo, Laikipia, Elgeyo Marakwet and Nandi) of Kenya. The assessment focuses on human security concerns arising from poverty, environmental degradation, political and human conflicts, health related problems, among others. Following an assessment and analysis of the human security threats in each of these counties, the study proposes intervention strategies that would reduce vulnerability and enhance human security among marginalized and disadvantaged groups in the target counties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".