The National Front Against Poverty: The Struggle for Income Redistribution
Bibliographic record
Abstract
This article describes the campaign developed by unions, human rights organisations and other social movements in Argentina at the end of the 1990s in order to push the government to implement measures to eliminate poverty and extreme poverty. It also refers to the results of this campaign in the following years, highlighting not only its direct impact but also its indirect consequences in the medium term, in particular on the public debates related to the social policies implemented by the new centre–left government since 2003. This campaign, popularly known as FRENAPO (National Front Against Poverty), was organised in the context of the implementation of neo-liberal macroeconomic policies in Argentina, which led the country to the biggest crisis in its history at the end of 2001. The members of the campaign proposed a package of economic and social measures oriented to unemployed workers (a basic income grant plus a professional education scheme), to the children and to the elderly (a basic income grant for both). The proposal was supported in a referendum by more than three million people all over the country during December 2001, but it was not considered by the Parliament. Although the alliance that supported FRENAPO eventually crumbled, the campaign was successful in its objective of influencing the public debates on how to respond to poverty and extreme poverty. Several measures implemented by the new centre–left government since 2003 were inspired by those debates, particularly those aimed at guaranteeing an income for children and the elderly. This article analyses the context of the campaign, identifies its concrete goals, origins and members, and explains how FRENAPO built power in order to achieve those objectives. Finally, it addresses the main consequences of FRENAPO, both in the short and long term, and highlights its lessons for future campaigns.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 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".