Locally led adaptation metrics for Africa: a framework for building resilience in smallholder farming sectors
Bibliographic record
Abstract
Kenya is one of several Sub-Saharan African countries vulnerable to climate change, which severely impacts their small-holder farming (SHF) sectors. To build resilience and reduce SHFs’ vulnerability to the impact of climate change, there has been ongoing advocacy for an increase in adaptation funds disbursed to these African countries. However, the effectiveness of adaptation funds relies heavily on the quality of metrics used for tracking and assessing adaptation needs and actions developed by SHFs. This study, which set out to evaluate the impact of existing locally led adaptation (LLA) metrics relevant to Kenya’s SHFs, systematically searched grey and journal articles published between 2007 and 2023 and found that these sources did not reveal the impact of LLA metrics on resilience of SHFs, nor did they provide a framework for developing adaptation metrics relevant to SHFs. Kenya’s SHF sector is strategically vital for both rural and national economies and is the lifeblood of vulnerable communities. To mitigate the impact of climate change on this sector, the present study developed the first framework for locally led adaptation metrics for SHFs by drawing on the context knowledge of Kenya’s SHFs and lessons from the resilience and adaptation policy literature. This framework requires five steps: (1) to carry out gender intersectionality analysis to unravel the diverse typologies of SHFs in Kenya in order to identify their adaptation needs; (2) to co-develop metrics with stakeholders, including SHFs, periodically reviewing their relevance; (3) to complement metrics with contextual data; (4) to develop a knowledge brokering platform for cross-community and cross-country learning; and (5) to connect with government and decision makers. While this study has provided guidance on implementing the locally led adaptation metrics for Africa (LAMA) framework in real-world settings, there is a need to explore further how quantitative metrics can be complemented with contextual data.
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 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 | 0.038 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.030 | 0.020 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".