Gender-Responsive Budgeting through the CBMS Lens
Bibliographic record
Abstract
This paper results from a series of international workshops that brought together CBMS and GRB practitioners to discuss how the community-based monitoring system (CBMS) can be used to facilitate gender-responsive budgeting (GRB) at the local level. To provide conceptual background to the discussion, the paper highlights two points where CBMS and GRB initiatives converge and complement each other. On the one hand, it points out that both serve as guideposts for government targeting and prioritizing of the poor and other vulnerable sectors of society. On the other hand, both are also centrally concerned with policymaking. CBMS was seen from the start as a tool to inform evidence-based policymaking while GRB emerged out of the realization that unless gender policies and plans have adequate accompanying budgets, they are not worth the paper they are written on. It also notes that the standard CBMS data already provide valuable input for GRBs (e.g., sexdisaggregated analysis of the situation of local people in terms of aspects such as education and economic activity and situation analysis of accessibility of services such as sanitation, nutrition and health). However, the potential of the existing instrument to support LLGRB work can be further enhanced.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".