Kenya Economic Update, November 2020 : Navigating the Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Kenya’s economy has been hit hard by \n COVID-19, severely affecting incomes and jobs. The economy \n has been exposed through the dampening effects on domestic \n activity of the containment measures and behavioral \n responses, and through trade and travel disruption \n (affecting key foreign currency earners such as tourism and \n cut flowers). Real Gross Domestic Product (GDP) contracted \n by 0.4 percent in H1 2020 year-on-year(y/y), compared to \n growth of 5.4 percent in H1 of 2019. This reflects a \n worse-than-anticipated Q2 GDP outturn, mainly due to a sharp \n reduction of services sector output, especially education. \n As a result, the economy is projected to contract by 1.0 \n percent in 2020 in the baseline scenario, and by 1.5 percent \n in a more adverse scenario. This revision essentially adopts \n the adverse scenario outlined in the April 2020 update, \n reflecting the more severe impact of the pandemic to date \n than had been initially anticipated, including on the \n measured output of the education sector following the \n closure of institutions in March. The special focus topic \n finds that the pandemic increased poverty by 4 percentage \n points (or an additional 2 million poor) through serious \n impacts on livelihoods, by sharp decreases in incomes and \n employment. The unemployment rate increased \n sharply,approximately doubling to 10.4 percent in the second \n quarter as measured by the KNBS Quarterly Labor Force \n Survey. Many wage workers who are still employed face \n reduced working hours, with average hours decreasing from 50 \n to 38 hours per week. Almost 1 in 3 household runbusinesses \n are not currently operating, and between February and June \n average revenue from household run businesses decreased by \n almost 50 percent. This has exacerbated food insecurity, and \n elevated pain and human suffering. In response to the \n crisis, the government has deployed both fiscal and monetary \n policies to support the healthcare system, protect the most \n vulnerable households, and support firms to help preserve \n jobs,incomes and the economy’s productive potential. Tax \n revenue dropped below target, due to the marked slowdown in \n economic activity, as well as tax relief as part of the \n government’s fiscal response package. At the same time, \n expenditures were raised to strengthen the capacity of the \n healthcare system to manage infections, protect the most \n vulnerable households, and support businesses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.016 | 0.011 |
| Research integrity | 0.001 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.042 |
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 it