The “Formally Feminist State”: A Potential New Player in the Inter-American Human Rights System?
Bibliographic record
Abstract
A decade ago, the Inter-American Court of Human Rights issued a landmark judgment in the case of González and Others (“Cotton Field”) v. Mexico, which addressed the abduction and subsequent sexual murder of three young women in the industrial border city of Ciudad Juárez—a place known for systematic gender violence and impunity. For the victims’ next of kin and the feminist and human rights activists involved in the litigation, the murders constituted feminicidios (feminicides). The resulting judgment has been celebrated not only for developing new standards for women's human rights internationally, but also for its domestic impact in the form of innovative feminist laws and policies in Mexico and other Latin American countries. With a focus on Cotton Field’s impact on Mexico, this essay explores the potential rise of the “formally feminist state”—a state that adopts domestic feminist legislation and policies but then resists their implementation—as a new player on the stage of the inter-American human rights system (IAS). Drawing on insights from American sociolegal analyses on judicial deference to the presence of policies and institutional mechanisms as indicators of compliance with antidiscrimination laws, I suggest that this new player may create a different set of challenges for courts in assessing states’ lack of compliance with norms on women's human rights.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.040 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".