Implementing Inclusion: Gender Quotas, Inequality, and Backlash in Kenya
Bibliographic record
Abstract
Abstract Extensive research has affirmed the potential of gender quotas to advance women's political inclusion. When Kenya's gender quota took effect after a new constitution was promulgated in 2010, women were elected to the highest number of seats in the country's history. In this article, we investigate how the process of implementing the quota has shaped Kenyan women's power more broadly. Drawing on more than 80 interviews and 24 focus groups with 140 participants, we affirm and refine the literature on quotas by making two conceptual contributions: (1) quota design can inadvertently create new inequalities among women in government, and (2) women's entry into previously male-dominated spaces can be met with patriarchal backlash, amplifying gender oppression. Using the ongoing process of quota implementation in Kenya as a case to theoretically question inclusionary efforts to empower women more generally, our analysis highlights the challenges for implementing women's rights laws and policies and the need for women's rights activists to prioritize a parallel bottom-up process of transforming gendered power relations alongside top-down institutional efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".