KENYA DURING THE START OF COVID-19: Response & Recovery Actions to Manage the Pandemic
Bibliographic record
Abstract
In this report, we seek to shed light on response and recovery actions to manage the COVID-19 pandemic that were undertaken in Kenya. Over the last ten years, the Government of Kenya has adopted a devolved system of government, delegating policy and services to county levels. The COVID-19 outbreak offered the country an opportunity to evaluate its pandemic preparedness and response capacity including the role of multisectoral coordination through its national disaster framework. Using findings from a report released by the Organization for Economic Co-operation and Development's (OECD) entitled Framework for Evaluating COVID-19 Responses (OECD, 2022), we explore how response and recovery phases were designed and implemented in Kenya in the early stages of the pandemic. Rather than an evaluation of OECD members' response decisions, the framework categorizes the various options implemented by countries, and we apply this as a guide as we reflect on Kenya’s own policy choices.
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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 | 0.005 | 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 | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".