Contested Landscapes: Collaborations Between Displaced Communities and International Advocacy Groups in Guatemala
Bibliographic record
Abstract
Abstract Purpose To assess how an indigenous community in Guatemala, displaced by a mining project, has collaborated with international human rights advocacy organizations to address chronic insecurity and vulnerability resulting from the violence of their displacement. Methodology/approach The research for this case study was gathered using unstructured interviews with Lote Ocho community members and human rights advocates as well as textual analysis of social media documents, press releases, and reports. Participant observation was conducted during a community forum. Human rights theory, post-conflict theory, disaster theory, and narrative economy frameworks informed the research. Findings As international human rights organizations collaborate with Lote Ocho to address the community’s displacement, intensive focus on a lawsuit between the community and a Canadian mining corporation HudBay Minerals, Inc., contributes to homogenization of the community, reinforcement of destructive power relationships, and lack of focus on long-term security. Practical implications Analysis of the potential harms of singular focus on legal action in the examined collaborations identifies areas for improvement for future collaborations in both Lote Ocho and other displaced communities. Originality/value Caal v. HudBay is the first case of its kind. Thus, the analysis presented here provides critical insight for international and community actors regarding the successes and shortcomings of collaboration in cases of development-forced displacement, identifying areas for improvement for future collaborations with displaced communities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.021 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".