Shifting Patterns of Fuel and Wood Use by Households in Rural Zimbabwe
Bibliographic record
Abstract
A questionnaire survey of fuel and wood use was administered to approximately 1500 households in rural Zimbabwe in 1994 and repeated in 1999. The nine localities covered by the survey fell into four strata distinguished by woodland cover, distance from urban centres and whether communal or resettlement (ex-commercial farming) areas. Over time household assets increased, but incomes remained constant in all but one stratum. Simultaneously wood became scarcer according to respondents. In all four strata firewood consumption fell markedly between 1994 and 1999. This was partially, but not entirely, due to switches to other fuels, either electricity near towns or non-wood biomass fuel in deforested areas further from towns. In other areas, non-wood biomass fuels declined considerably. Kerosene use showed mixed patterns, with decreases in the numbers of consumers but increased rates of consumption. Wealthier households were more likely to use modern fuels such as kerosene for cooking, candles and electricity. The general reduction in firewood consumption entailed changes in collection practices and increased purchase of wood, but it is not clear how fuel use efficiency was improved by such a great margin. Utilisation of wood for construction also declined over the five year period.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".