Gender Quotas and Women's Substantive Representation: Lessons from Argentina
Bibliographic record
Abstract
This article integrates the comparative literature on gender quotas with the existing body of research on women's substantive representation. Quota laws, which bring greater numbers of women into parliaments, are frequently assumed to improve women's substantive representation. We use the Argentine case, where a law mandating a 30% gender quota was adopted in 1991, to show that quotas can affect substantive representation in contradictory and unintended ways. To do so, we disaggregate women's substantive representation into two distinct concepts: substantive representation as process, where women change the legislative agenda, and substantive representation as outcome, where female legislators succeed in passing women's rights laws in the Argentine Congress. We argue that quota laws complicate both aspects of substantive representation. Quotas generate mandates for female legislators to represent women's interests, while also reinforcing negative stereotypes about women's capacities as politicians. Our case combines data from bill introduction and legislative success from 1989 to 2007 with data from 54 interviews conducted in 2005 and 2006. We use this evidence to demonstrate that representation depends on the institutional environment, which is itself shaped by quotas. Institutions and norms simultaneously facilitate and obstruct women's substantive representation.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".