A Model for Understanding CEDAW’s Impact on Implementing Gender Equality Reforms: Lessons from Canada and India
Bibliographic record
Abstract
This Article provides a model for examining the impact of the Convention on the Elimination of All Forms of Discrimination Against Women (“CEDAW”) on implementing gender equality reforms using Canada and India, two CEDAW State Parties, as case studies. It also explores the influence of heteropatriarchy, deeply-rooted cultural norms perpetuating gender inequality, on hindering CEDAW’s ratification in the United States, as well as CEDAW’s effectiveness in implementing reforms in Canada and India. The analysis showcases how non-governmental organizations (“NGOs”) in these countries have nevertheless achieved limited successes through their mobilization of CEDAW to address specific gender injustices, such as gender violence against Indigenous women and workplace sexual harassment. However, even if CEDAW facilitates a state’s enactment of reforms, the enactment may not result in the reforms’ implementation because the treaty alone cannot overcome state-sanctioned misogynistic beliefs. The research contributes to the scholarly dialogue regarding CEDAW’s effectiveness in three ways. First, this Article provides a model for understanding CEDAW’s impacts on implementing gender equality measures through a comparative analysis of such measures’ implementation in Canada and India post-treaty ratification. Second, this Article uses this comparative lens to argue that, although the United States should ratify CEDAW, its potential impact rests in its ability to advocate for gender equality reforms on which society generally agrees while avoiding controversial reforms (e.g., legalizing abortion). In so doing, the research takes the scholarly debate beyond advocating for or against the United States’ ratification of CEDAW based on the assumption that it will or will not make a difference in women’s lives. Instead, this Article argues scholars cannot address that question until they can show a state has successfully implemented a CEDAW-inspired reform. Finally, this Article aims to provide peace of mind to CEDAW’s ratification opponents in the United States by suggesting that CEDAW, if eventually ratified, will only result in reforms on which there is bi-partisan support (e.g., more support for mothers and pregnant women in the military) rather than reforms on divisive issues because of entrenched patriarchal beliefs in American society.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".