Research on Non-profit Organizations’ Participation in the Antipoverty Problem in Argentina
Bibliographic record
Abstract
The continuity, concealment and complexity of poverty in Argentina increased the difficulty of poverty governance, and the defects of the government and market in the process of dealing with the poverty also set obstacles for the governance of the poverty. In order to make up for the shortage of the government and the market, non-profit organizations with a profound philanthropic cultural tradition participated in the anti-poverty process in Argentina and played a positive role in promoting public services, providing employment assistance, offering technical assistance, developing agricultural markets and rights relief. In this process, the non-profit organizations have established cooperative relationships with public and private organizations respectively, and strengthened interactions with volunteers and the underprivileged group. In the process of fighting against poverty in Argentina, non-profit organizations have demonstrated the characteristics of grassroots and mediation, and played the role of participants in the poverty reduction program of the government, social resource mobilizers and interest coordinators. The experience of non-profit organizations in Argentina in anti-poverty is worthy of attention and reference, but the restriction of resources and government authority limits the role of non-profit organizations, and the disadvantages of non-profit organizations themselves also cause some negative effects.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".