Bibliographic record
Abstract
Abstract Over the past decade, Ecuador, Bolivia, and Chile have been buffeted by intensive transformations. Political scientist Pascal Lupien here reveals how Indigenous political activists responded to these changes as part of their long, ongoing struggles for equal citizenship rights and economic and political power. Such activists are often thought to rely solely on disruptive, large-scale forms of collective action, but Lupien argues that twenty-first-century Indigenous activists have turned toward new modes of fostering Indigenous civil society. Drawing on four years of immersive, community-engaged fieldwork with more than ninety Indigenous organizations and groups within and across three countries, Lupien shows how Indigenous organizations today are newly pursuing, adapting, and sustaining local activism in a globalized, technology-centered world. He reveals that Indigenous groups have effectively built on older twentieth-century technologies—for example, radio, TV, and print media—by adapting social media technologies in ways that are unique to their political identities and day-to-day needs. In the context of increasing recognition of global Indigeneity, Lupien's capacious, descriptive work contributes to understanding Indigenous peoples’ contemporary struggles, the evolving and unique nature of Indigenous civil society, and the return to large-scale resistance in 2019 that resulted in the largest uprisings in a generation.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".