From sub-IDOs to Impact: A Guide to Developing Gender-related Policy Indicators in CCAFS
Bibliographic record
Abstract
The Gender and Social Inclusion (GSI) unit of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) is dedicated to the advancement of gender-responsive climate policies. This document is a guide to best practices for developing indicators to track progress toward CCAFS gender-related policy sub-intermediate development outcomes (sub-IDOs) and gender activities at project and national levels. While the primary objective of this guide is to be used as a practical resource for CCAFS projects in tracking progress towards CCAFS gender-related policy sub-IDOs, the methodology and frameworks developed herein would be useful for other programs in selecting which gender issues should be prioritized in climate policy and how these can be instrumentalized in tracking through appropriate and meaningful gender indicators. This guide also provides a synthesis of best practices and recommendations for tracking gender outcomes in climate policy by drawing upon both extant literature and project experiences revealed by CCAFS project leaders and experts (n=14). A discussion of the limitation of gender indicators and how they can be complemented with other tools and methods is also included.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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".