Agriculture: Future Scenarios for Southern Africa
Bibliographic record
Abstract
This case study examines the agricultural policies that Zimbabwe uses to ensure food security and how national efforts affect the future scenarios of regional food security in Southern Africa. The Zimbabwean agricultural system is the most publicly assisted sector of the economy. Its output does not, however, match the extent of the support it receives, as the country’s food security situation is worsening. The case study critically analyses field data in the hyperinflationary economic environment that currently pertains in Zimbabwe. The results of the case study indicate that Zimbabwe’s food security is on a steep decline, implying a negative contribution to Southern Africa’s food security. The case study recommends additional domestic and trade policy measures to improve investment and development in agriculture for the benefit of Zimbabweans and the region as a whole. © 2009 International Institute for Sustainable Development (IISD) Published by the International Institute for Sustainable Development International Institute for Sustainable Development 161 Portage Avenue East, 6th Floor Winnipeg, Manitoba Canada R3B 0Y4 Tel: +1 (204) 958-7700 Fax: +1 (204) 958-7710 E-mail: info@iisd.ca Web site: http://www.iisd.org/ Agriculture: Future Scenarios for Southern Africa – A Case Study of Zimbabwe’s Food Security Evangelista Mudzonga Tendai Chigwada Agriculture: Future Scenarios for Southern Africa – A Case Study of Zimbabwe’s Food Security trade knowledge network
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".