Anthropology and mediation in an environmental conflict: Worldview translation as synthesis
Bibliographic record
Abstract
Anthropological research can help to transform social conflict. This chapter illustrates the case by studying the environmental conflict surrounding the Site C Clean Energy Project , a hydroelectric dam project in northern Canada. Although there is an official mediation process on the construction project between the company, a favorable population, an opposition population, and the indigenous population. However, this mediation does not consider the different worldviews or even understandings of the world that underlie the different perspectives. This chapter introduces the approaches of Worldview Analysis and Worldview Translation . Anthropological research uncovers different world views and contributes to a better mutual understanding. However, this study also shows the limitations of such an approach. The presented case is dominated by significant power imbalances between the participants and thus by forms of structural and cultural violence. Even supporting anthropological research cannot balance this out. The researcher also faces enormous demands for staying all-party and not simply taking sides with the weaker party. The dominant discourse will always frame the benefits of the dam project as community and social benefits and, in contrast, treat the opponents’ objections as singular cases. This chapter reviews current seminal literature on anthropology&s;s engagement in conflict transformation and research that aligns with the discipline&s;s contemporary advocacy approaches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".