Do governments account for gender when designing their social protection systems? Findings from an analysis of national social protection strategies
Bibliographic record
Abstract
Abstract The negative impacts of the COVID‐19 pandemic on women's employment, care responsibilities, and access to services have motivated an unprecedented level of advocacy around strengthening national social protection systems from a gender perspective. Yet very little empirical evidence exists about what a gender‐responsive social protection system entails in practice. This paper addresses this gap through a comparative analysis of 52 national social protection strategies from primarily low‐and middle‐income countries. To analyse the gender responsiveness of these strategies, we developed an analytical framework based on international human rights standards and social policy, gender and development literature. Through presentation of the framework and our findings, this paper makes several contributions to scholarship and practice. First, our framework―the first of its kind―offers a novel conceptual and methodological contribution by enabling a systematic, comparative assessment of national approaches to social protection from a gender perspective. Second, the framework enables the systematisation of empirical evidence on the degree to which gender is integrated into social protection systems. By applying it to national social protection strategies, we identify which gendered risks and vulnerabilities are most commonly acknowledged and addressed in countries' efforts to create gender‐responsive social protection systems. We also highlight a concerning gap between rhetoric and response when it comes to gender equality in the strategic plans that governments lay out for these systems. To our knowledge, this is the first systematic cross‐country assessment of such documents. We conclude with future directions for research and practice, including the gap between recognition and action.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".