Recognition Politics: Indigenous Rights and Ethnic Conflict in the Andes <i>by Lorenza B. Fontana</i>
Bibliographic record
Abstract
Without a doubt, one of the most promising developments in Latin America over the past thirty years has been the rise of Indigenous movements and the expansion of democracy to include Indigenous actors and issues. Fontana's book is about what happens next now that Indigenous groups are relatively entrenched as social and political actors, or what the author terms the “post-recognition phase” (14). The book tells the story of the non-Indigenous, rural, poor communities who have been left behind by international recognition politics. Without recourse to Indigenous identity and living in an environment of resource scarcity, building resentment by peasant groups has translated into intercommunal conflicts with Indigenous peoples in the featured cases of Colombia, Peru, and Bolivia. In an era of growing strife and instability in the Andes, this book will make Indigenous politics scholars stop and think about the potential ramifications of our work. The central thesis of the book is that recognition reforms that advance Indigenous peoples’ rights to the exclusion of other rural folk can produce “recognition conflicts” in the form of violent disputes between peasant groups and Indigenous peoples (4). This is a controversial argument, one that runs counter to the literature on multiculturalism and Indigenous rights. Based on extensive field work observations carried out in the three country cases, Fontana, to support her points, provides paradigmatic examples of disputes between Indigenous people and peasant communities in issue areas ranging from territorial control and prior consultation to bilingual intercultural education. In one example, a 2007 protest in the Bolivian Amazon saw hundreds of disaffected peasants march into an Indigenous territory and national park with chainsaws, threatening to clear cut the forest in response to the state's granting of the land title to an Indigenous organization. In another example, an illegal migrant settlement within an Indigenous territory in the Peruvian Amazon was attacked by members of the local Awajún community in a 2002 skirmish that left sixteen people dead (160). The author makes a compelling case for the need for more scholarly attention to the horizontal dimensions of contentious Indigenous politics in a literature that is dominated by a focus on the vertical conflicts between Indigenous peoples and the state.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".