Socioeconomic Analysis of Rural Credit and Technical Assistance for Family Farmers in the Transamazonian Territory, in the Brazilian Amazon
Why this work is in the frame
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Bibliographic record
Abstract
<p>In Brazil, Rural Credit and Technical Assistance policies for family farming were formulated with the goal of promoting rural development in a sustainable and integrated manner. This study is the result of the <em>Monitoring and assessment of public policies for territory management in the Pará Amazon</em> project, undertaken by the Federal University of Pará (UFPA), aimed to evaluate the main socioeconomic impacts and limitations for the execution of these policies in the Transamazonian Territory. It is characterized as qualitative and exploratory, developed from bibliographic research and field research, based on data obtained through interviews conducted with 22 families of farmers who are beneficiaries of Rural Credit, the B modality of the National Programme for Strengthening Family Agriculture (PRONAF) and of the Technical Assistance Policy, whose sample corresponds to 10% of total contracts made effective within that Territory, between the years of 2013 and 2014. In addition to these farmers, for the analysis of the Technical Assistance service, interviews were conducted with extension workers from eight organizations, one of which is a state public company and seven of which are outsourced companies hired by the Federal Government to provide this service. The descriptive analysis shows that PRONAF B focuses on areas that produce short cycle food crops and on fishing activities. The technical assistance service provided by the public company is carried out in all the cities within the Territory, but only meets 10% of the demand; the service provided by the outsourced companies also occurs in all cities and its greatest setback is the delay in the release of funds by the Federal Government, which generates delays in the agricultural calendar and discontinuity in the productive activities, due to the end of the term of the companies’ contracts.</p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it