Gender Budgeting Implementation in Italian Regional Governments: Institutional Behavior for Gender Equality and Women’s Empowerment
Bibliographic record
Abstract
Gender budgeting has great potential to promote the United Nations 2030 Agenda concerning gender equality and women’s empowerment. This article shares some reflections on the need to implement and institutionalize gender budgeting at the regional level, both by embedding gender issues into the overall regional government budgetary process and by promoting gender equality disclosures. An empirical insight into the institutional behavior of Italian regional governments is provided. The study seeks to understand how the gender perspective is integrated into the governmental strategy that informs the entire budgetary cycle of Italian regional governments, by performing a thematic analysis of the key regional planning documents. The local promotion of gender budgeting implementation through institutional norms and the practice of gender performance reporting in Italian regional governments are also addressed. The results highlight that although there are differing degrees of commitment to gender equality and women’s advancement within the regions, the gender perspective is quite homogeneously integrated into the governmental strategy. Four gendered transversal thematic priorities are identified: the encouragement of women’s employment, the promotion of equal gender opportunities, the enhancement of social inclusion, and the combatting of gender-based violence. Furthermore, although nine regional laws establish gender performance reporting, additional reporting tools integrating non-financial information on gender issues are included solely in a small part of the regional government performance reporting systems. A greater organizational and cultural commitment to the institutionalization of the gender budgeting idea is needed in order to allow stakeholders to appreciate the government’s value outcomes in all their dimensions, including the gender-related social dimension.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".