Enhancing climate services design and implementation through gender-responsive evaluation
Bibliographic record
Abstract
Assessing and responding to gender inequalities, and promoting women's empowerment, can be critical to achieving the goals of climate services, such as improved climate resilience, productivity, food security and livelihoods. To this end, our paper seeks to provide guidance to rural climate service researchers, implementing organizations, and funders on gender-responsive evaluation of climate services, including key questions to be asked and appropriate methodology. We draw on case studies of rural climate services in Mali, Rwanda and Southeast Asia to illustrate how gender-responsive evaluations have framed and attempted to answer questions about climate information needs, access to information and support through group processes, and contribution of climate services to empowerment. Evaluation of how group participatory processes can enable women's and men's demand for weather and climate information can help close knowledge gaps on gender equity in access to climate services. Quantitative methods can rigorously identify changes in demand associated with varying interventions, but qualitative approaches may be necessary to help assess the nuances of participatory communication processes. Furthermore, evaluation of how women's and men's information needs differ according to their roles and responsibilities in distinct climate-sensitive decisions can help assess gender inequities in climate services use. Evaluation that critically considers the local normative and institutional environment influencing empowerment can help identify pathways for climate services to contribute to women's empowerment. Qualitative and mixed method methodologies can be helpful for assessing the normative and institutional changes upon which empowerment depends. Although evaluations are often conducted too late to inform the design of time-bound projects, they can contribute to improvements to climate services if results are shared widely, if implementers and funders consistently factor evidence and insights from prior evaluations into the design of new initiatives, and if ongoing climate service initiatives conduct preliminary evaluations regularly to support mid-course adjustments.
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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 | 0.200 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".