Can Applying a Gender Lens to Social Innovation Promote Women's Rights and Gender Equality?
Bibliographic record
Abstract
Social innovation is not new, but it is increasingly being called on to provide solutions to some of the world’s most pressing social and economic problems. Despite awareness about its importance, research in the field of social innovation is often vague, and there are competing definitions and understandings of the concept. There is also very little research that attempts to connect the field of social innovation with the fields of gender studies, women’s studies, feminist research, or men and masculinity studies. This dissertation applies a gender lens to the concept of social innovation. In doing so, it aims to develop the foundations for future research at the intersection of social innovation and gender equality. To conduct this research, I sought an affiliation with the MATCH International Women’s Fund, a Canadian organization that provides grants to support women’s social innovations in the Global South. This is a qualitative exploratory study in which I used peer-reviewed academic research as well as practitioner tools and knowledge from a range of sectors and disciplines. These include the results of 25 in-depth interviews with people engaged in social innovation or a related field, data from Twitter Canada, and a fellowship experience at Canada’s leading innovation hub, MaRS Discovery District. My research demonstrates the need for gender sensitivity and analysis in the field of social innovation. I argue that social innovation will not achieve its full potential if it does not understand how to respond to existing gender hierarchies all over the world. Innovation is about bringing together different perspectives; when we leave an analysis of gender out, we miss out on a lot. A gender analysis is not as simple as including more women in innovation; it is also about how innovation is interpreted and understood.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".