Climate change & human mobility : the interconnectedness of colonial, contemporary development policies, and climate change in shaping human mobility in Zimbabwe’s small-scale rural farming areas
Bibliographic record
Abstract
Climate Change and Human Mobility; The interconnectedness of colonial, contemporary development policies, and climate change in shaping human mobility in Zimbabwe's smallscale rural farming areas"By Trymore Maganga Climatic and non-climatic factors affect small-scale agriculture leading to high cases of food insecurity and subsequent human mobility in Zimbabwe's communal areas.These farming areas were established by the colonial British government for African farmers during the colonial era, and livelihoods in these marginal areas have endured a long history of poverty, poor agricultural production, and underdevelopment.Communal farming areas still exist today and continue to shape the livelihoods of most African farmers, subjecting them to high cases of food insecurity and poverty.Compounding these livelihood challenges in communal areas, are contemporary global and national economic development decisions brought about by the international community and the new African government in Zimbabwe after independence.The turn of the 20 th century has seen climatic factors working together with other human mobility drivers in exacerbating the existing food security challenges, increasing the imperative of people to move in Zimbabwe.The motives behind human mobility have been working together to create a complex web of mobility patterns in Zimbabwe's communal areas.Unfortunately, there are no peer-reviewed studies explaining this interplay of multicausal factors contributing to human mobility patterns in Zimbabwe.As a result, this study examines the interconnectedness of climate change, colonial and contemporary development policies in shaping modern-day human mobility patterns in Zimbabwe.I argue that climate change adaptation challenges in communal areas of Zimbabwe cannot be addressed without first dealing with the complex political and historical context of the country.Additionally, climate change is being ignored in human mobility studies in Zimbabwe, and there is a need to seriously consider climatic factors alongside other traditional human mobility drivers in the country.Lastly, human mobility as a climate adaptation strategy should be incorporated into the country's climate policy framework, mimicking a strategy at the national level that has been effectively used by households at the local level to cushion themselves from climate and livelihood related stresses over the years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".