COVID-19 Lockdown in a Kenyan Informal Settlement: Impacts on Household Energy and Food Security
Bibliographic record
Abstract
Abstract A COVID-19 lockdown may impact household fuel use and food security for ∼700 million sub-Saharan Africans who rely on polluting fuels (e.g. wood, kerosene) for household energy and typically work in the informal economy. In an informal settlement in Nairobi, surveys administered before (n=474) and after (n=194) a mandatory COVID-19-related community lockdown documented socioeconomic/household energy impacts. During lockdown, 95% of participants indicated income decline or cessation and 88% reported being food insecure. Three quarters of participants cooked less frequently and half altered their diet. One quarter (27%) of households primarily using liquefied petroleum gas (LPG) for cooking before lockdown switched to kerosene (14%) or wood (13%). These results indicate the livelihoods of urban Kenyan families were deleteriously affected by COVID-19 lockdown, with a likely rise in household air pollution from community-level increases in polluting fuel use. To safeguard public health, policies should prioritize enhancing clean fuel and food access among the urban poor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".