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Record W1492733652

Agriculture: Future Scenarios for Southern Africa

2009· article· en· W1492733652 on OpenAlexaboutno aff
Evangelista Mudzonga, Tendai Chigwada

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsFood securityAgricultureBusinessSustainable developmentEconomic growthPolitical scienceGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.020
GPT teacher head0.197
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2009
Admission routes1
Has abstractyes

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