Mourning and Mobilization in the Americas
Bibliographic record
Abstract
Shows how communities across the Americas transform their grief over murdered and missing trans and non-trans women, girls, and two-spirit people into powerful social movements that challenge state violence and demand justice. A groundbreaking and transnational examination of gender-based violence, Mourning and Mobilization in the Americas reimagines how we understand the relationship between grief and political action. Lydia Huerta Moreno brings together the work of activists, scholars, artists, writers, and influencers from 1994 to 2023 to chronicle the intersection of activism with the rise of social media and the eventual implementation of legislation codifying woman killing as a crime. Expanding the concept of feminicide to encompass trans women, two-spirit people, and missing and murdered women and girls across the Americas, Huerta Moreno illuminates the deep connections between different forms of gender-based violence across the Americas and weaves together questions of race, class, gender, and immigration status. Through innovative and sensitive analysis of postmortem politics, the book reveals how communities transform profound loss into powerful social movements, from Mexico to Brazil to the United States and Canada and beyond. With a foreword by Sayak Valencia, Mourning and Mobilization in the Americas is a must-read for activists, scholars, and anyone concerned with human rights, revealing how grief can spark resistance against systemic violence and government inaction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".