Determinants of Adaptation to Climate Change: A Gendered Analysis from Bahi and Kondoa Districts, Dodoma Region, Tanzania
Bibliographic record
Abstract
Although various long term adaptation measures are currently implemented by farmers to adapt to the effects of climate change in Tanzania, information regarding factors determining choice of adaptation options between men and women is scarce. A gendered analysis was done to analyze determinants of adaptation to climate change in Bahi and Kondoa Districts, Dodoma Region, Tanzania. A cross-sectional research design was adopted whereby the data was collected from a sample of 360 respondents, 12 focus groups and 18 key informants. Analysis of quantitative data involved descriptive statistics and multinomial logit model using Nlogit 3.0 and qualitative data were summarized by using content analysis. Results revealed that the main occupation and land size were the main factors that determined adaptation options for men during food shortage while for women, the main factor was marital status. The village/location of respondents was the main factor that determined climate change adaptation option for women to adapt crops to climate change whereas, for men, access to agricultural knowledge was the main factor that encouraged men to use improved seeds, manure and deep cultivation, instead of selecting and keeping enough seeds for the next season. It is concluded that factors determining choice of climate change adaptation between men and women are not the same, emphasizing the need for gender differentiated interventions to promote climate change adaptation. Thus, planners and policy makers from Agriculture, Livestock and Environment sectors; Tanzania NAPA and other development practitioners dealing with climate change should use gender sensitive interventions to manage climate change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".