Zimbabwe's Drought Relief Programme in the 1990s: A Re‐Assessment Using Nationwide Household Survey Data
Bibliographic record
Abstract
Zimbabwe's Drought Relief Programme was hailed in the 1980s and 1990s as an effective response to a food crisis in a poor country. International observers in particular credited the Programme with preventing famine and protecting livelihoods. Even before the current political turmoil and the ensuing politicisation of Drought Relief that have afflicted Zimbabwe since 2000, Zimbabwean authors were more sceptical about the effectiveness of Drought Relief. Both sides in the debate, however, failed to substantiate their arguments with national household survey data on who got what kind of assistance from Drought Relief, but rather relied on administrative data, qualitative interviews or sub‐national surveys. Drawing its inspiration from WHO's minimum evaluation procedure, this article uses data from four nationwide household surveys in 1992–1993 and 1995–1996 and various definitions of poverty to ask whether Drought Relief provided poor people with relevant, timely and adequate assistance in the 1990s. The analysis suggests that Drought Relief was effective in supporting drought‐affected smallholders during the 1990s. Drought Relief generally had a slight pro‐poor bias. Unfortunately, Drought Relief since 2000 has a very different character.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".