Overcoming Silencing Practices: Indigenous Women Defending Human Rights from Abuses Committed in Connection to Mega-Projects: A Case in Colombia
Bibliographic record
Abstract
Abstract Many of those who dare to raise their voices in defence of human rights in response to abuses committed in connection to mega-projects are being repressed in the Americas. In this context, Indigenous women leaders face multiple forms of violence, including gender-based violence. The prevailing narrative of ‘progress’ and ‘development’ that accompanies mega-projects in the region often stands in stark contrast to their lived experiences, as Indigenous women human rights defenders frequently face silencing practices from companies, authorities and other groups including paramilitary forces. In this article, I contend that Indigenous women leaders have managed to overcome the silence that is being imposed on them. But what are silencing practices? What does gender-based violence mean in this context? How do Indigenous women leaders overcome silencing practices? The article responds to these questions by focusing on the Wayúu Women’s Force mobilization in Colombia and drawing on the emerging ‘braided action’ theoretical framework.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".