Drivers of Disparity: How Policy Responses to COVID-19 Can Increase Inequalities
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
Countries across the world have deployed macroeconomic policies to address the negative economic implications of the COVID-19 pandemic and subsequent lockdown measures. However, these policies can have different outcomes for various segments of the population. This policy briefing assesses the channels through which macroeconomic policy responses in Nigeria and Uganda negatively affect or exclude specific groups, with the aim of resetting policies to achieve more inclusive outcomes that will support economic growth and development in the post COVID future. It finds that the urban poor and the informal sector are being excluded as a result of the poor coverage of cash transfer programmes and the implementation of policies mostly applicable to the formal sector. Loans to low-income borrowers are not likely to increase despite downward revisions to the monetary policy rate, while importers and poorer households will be the worst hit by exchange rate adjustments in Nigeria. While the middle class and rich are affected by the removal of subsidies in Nigeria, those living in poverty do not benefit from the budget restructuring in Uganda.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it