The Zapatista Effect: Information Communication Technology Activism and Marginalized Communities
Bibliographic record
Abstract
This paper will demonstrate how access to relatively inexpensive Information and Communication Technologies (ICT) has allowed marginalized communities to bypass traditional channels and agitate for political changes. Widespread ICT usage has allowed marginalized peoples not only to disseminate their views, but to build grassroots alliances with similarly minded groups. Access to ICT has allowed groups in countries such as Burma, China and Sudan the freedom to share information that may otherwise be suppressed. \n \nThe ability to freely access and disseminate information is a considered a fundamental right in most free and democratic societies. In many countries, this principle fails to translate into reality. Many of the world’s governments actively constrain their citizens from information access to the commons. Marginalized groups are denied participation in decision-making processes and are ignored by traditional media. They often live in “ICT Poverty”, a state in which little information flows into or out of their communities. As a result, these peoples are denied their ability to benefit from their citizenship rights. \n \nParticular attention will be paid to indigenous groups such as the Zapatista Army of National Liberation, a largely Mayan group from the impoverished state of Chiapas, Mexico. The Zapatistas burst onto the scene in 1994 and used the Internet to build a trans-national solidarity network among human rights groups. The media spectacle they created forced the Mexican government to negotiate with Zapatista communities over issues such as land rights, compensation for resource extraction and indigenous political autonomy. \n \nMany other marginalized groups have used the Zapatista model to overcome social barriers and improve local conditions. Perhaps the most important use of ICT is to raise awareness and build relationships with advocates in other nations. The use of ICT by these groups can have a major impact on global coverage of events and help create public pressure to change policy. As information professionals, what are our responsibilities in regards to bridging the gap between Canadian libraries and marginalized peoples in the international community?
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".