Indigenous Movements, Collective Action, and Social Media: New Opportunities or New Threats?
Bibliographic record
Abstract
Indigenous peoples remain among the most marginalized population groups in the Americas. The decline of the Indigenous protest cycle in Latin America by the mid-2000s meant that research on collective action turned elsewhere just as the use of social media was becoming more prominent in the tactical repertoire of collective action, and we know little about how Indigenous groups have adapted new technologies for the purpose of civic engagement. If social media has begun to take the place of disruptive action (the most effective tactics in the 1990s according to Indigenous leaders), if personalized action is replacing collective identity (a strength of the Indigenous movements in the 1980s–1990s) and if their access to technology is limited, what does this mean for the ability of Indigenous communities to pursue their claims? Based on 2 years of fieldwork, this article addresses this question from the perspective of Indigenous organizations in three Latin American countries, Bolivia, Chile, and Ecuador. We find that some Indigenous organizations have benefited from the use of information and communication technologies (ICTs) in terms of enhanced communication, access to information, visibility, interest promotion, and commercialization of products and services. At this point in time, however, it appears that the disadvantages outweigh the benefits.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".