Women-Led Social Innovation Initiatives Contribute to Gender Equality in Rural Areas: Grounded Theory on Five Initiatives From Three Continents
Bibliographic record
Abstract
Abstract Ensuring gender equality is an important development challenge, especially in rural areas, where women are often marginalized by economic, socio-cultural and policy structures. Women-Led Social Innovation Initiatives (WLSIIs) are a promising way to address this challenge, but their contributions to gender equality depend on complex interactions between marginalizing structures and agency of women. The objective of this paper is to examine how the relevant elements of agency enable WLSIIs to contribute to progress towards gender equality in rural areas. We examine five WLSIIs located in Canada, Italy, Lebanon, Morocco, and Serbia. The cases focus on employment, education, identity, gender roles, and rural development, and are analyzed by grounded theory. We identified 1) gendered identity, 2) (in)dependence of women, and 3) control of women over the “rules of the game” as structural features that can enable or constrain WLSIIs. These concepts are located between grand societal structures (policy, economy, culture, and social organization) and women’s concrete, everyday realities, and as such helped us to understand factors supporting or hindering women’s agency and well-being. We identified women’s self-confidence, women-to-women networks, and self-developed and externally supported capacity as the key elements enabling agency. All these together increased social acceptance of the examined WLSIIs, helping to overcome cultural prejudices and gendered stereotypes. For example, women-to-women networks and self-organization increased economic independence, which reduced skepticism towards “new” roles of women and even changed unequal political dynamics. We conclude that women’s collective agency can be enabled by WLSIIs in diverse geographical and cultural contexts and should be recognized by policymakers as a key mechanism that has great potential for enhancing gender equality and overcoming structures marginalizing rural women.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".