Indigenous women leading the defense of human rights from abuses related to mega-projects: Impacting corporate behavior — overcoming silencing practices
Bibliographic record
Abstract
In the face of extreme violence, some Indigenous women-led social movement organizations that defend human rights in the context of abuses committed in connection to mega-projects have achieved favorable changes in corporate practices (success). In the predominantly patriarchal, capitalist and racist context of Latin America, what explains the success (or not) of Indigenous women-led mobilizations regarding the most politically and economically powerful actors in the world? My doctoral study is dedicated to responding to this question. In this article, I offer a very brief overview of that study. Thus, I provide some details about my research model in order to then introduce the acción trenzada theoretical framework that emerges from it. In light of that framework and the case of Lenca leader Berta Cáceres in Honduras, I next discuss aspects of a dynamic of forces where criminalization as a silencing practice is used against mobilizations led by Indigenous women human rights defenders, and how they are overcoming it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".